Water is an amazing substance. The human body is made up of more than 50% water. Approximately 70% of the earth’s surface is covered by water. We drink it, bathe in it, cook with it, clean with it. It is also used to cool data centres, which is an increasing draw on what is a scarce resource in many parts of the world.

The boom in artificial intelligence (AI) has been fuelling the construction of new data centres worldwide. This is driven by the massive computing workloads required to train and run generative AI models. AI data centres are specialised facilities with the infrastructure needed to support the increased computational demands.

A recent UN report warned that water used by AI data centres is expected to equal the everyday water needs of 1.3 billion people by 2030. Currently, air conditioning systems carry cool air to the server rooms housing the electronic equipment. Water is then used to carry away waste heat from the air to the environment.

But are there other potential ways to cool these facilities, ways that may not consume as much water?

For engineering purposes, water has extraordinary thermal properties. It has a very high specific heat capacity (the energy required to raise its temperature). It also has an exceptionally high latent heat of evaporation (the energy required to turn liquid water into water vapour).

Latent heat is what cools you when sweat evaporates from your skin, an astonishingly powerful cooling mechanism. These properties make water the ideal medium to transport heat, for applications ranging from automobiles and home heating, to large scale thermal power stations.

If you step inside a data centre, your senses are struck by the noise of cooling fans and the feel of swirling airflow on your skin.

The reason that air is used for direct cooling of the equipment is that it is safe, compared to a liquid like water, which could leak and damage electronics. In comparison with water, however, air is very poor at transporting heat even when given a helping hand by noisy cooling fans and churning air flows. The energy required to cool data centres is comparable in scale to the energy consumed by the electronic equipment itself.

Water is used to extract the waste heat from the air conditioning system through something called a chilled water loop. Hot air from the servers is passed over cold water coils. The now warm water is then piped outside the server hall.

A structure called a cooling tower is used to remove this heat from the chilled loop. The water to be cooled is brought in contact with ambient air as a spray, and a portion of it evaporates.

This cools the rest of the water, which is then recirculated. There are other flavours of data centre cooling which also exploit the latent heat of water – using the direct and indirect evaporative cooling of outside air. However, these also consume lots of water.

Is water consumption a bad thing?

Evaporative cooling places a demand on water supplies, but it also serves to reduce electrical energy consumption of the overall cooling infrastructure. In this context, it is important to note that electricity generation itself also requires water.

I am a professor at the University of Limerick, so for Ireland’s mix of energy sources, approximately 0.1-2 litres of water are required per unit of electricity (L/kWh).

Hence, by reducing electricity demand, evaporative cooling could be regarded as a “least bad” option in terms of the overall consumption of energy and water resources.

Of course, sufficient sources of water are required – clearly a critical factor in many parts of the world – and water stress is an issue even in rainy Ireland. However, there is some evidence of collected rainwater being used to reduce the demand on the water network here.

The use of air as the primary coolant for the electronic equipment significantly influences the overall energy consumption of a data centre. Because air is such a poor coolant, it only heats up by 5-20°C when it passes through the equipment.

The cooling infrastructure has to remove large quantities of thermal energy at relatively low temperatures – in the range of 25-40°C. Heat transfer is driven by temperature differences, hence the cooling plant is large, and it requires a lot of electricity to run the associated fans, pumps and compressors.

What if liquid was used to cool the electronic equipment using a “direct-to-chip” scheme? In this case, sealed pipe networks bring the coolant into the equipment, to extract heat from the power-hungry CPUs and GPUs (microprocessors and graphics processors) at the heart of servers.

Here, because a liquid such as water is far superior to air, the water being piped away from the equipment is hotter – closer to the component temperature limits, and potentially 60°C or higher.

Direct-to-chip schemes do reduce the cost of cooling, and offer opportunities to make use of waste energy in the form of heat carried away by water from the server equipment.

So why haven’t all data centres moved to this technology? Some are already using direct liquid cooling, but there may be some resistance to retrofitting an “air driven” facility. The replacement of electronic equipment in data centres is fairly frequent (between 2-7 years), allowing the transition to “direct-to-chip”. But there are costs involved in making the switch and some air cooling needs to be retained for certain classes of equipment.

Direct-to-chip systems would require a smaller, more frugal infrastructure.

Data centres now draw such vast quantities of electrical energy – over 7.5 TWh in today’s Ireland, almost a quarter of metered consumption – that it is imperative to reduce consumption and reuse the resource where possible.

Jeff Punch does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Original source: https://analysis1.mil-osi.com/2026/08/19/theres-a-better-way-to-cool-data-centres-that-cuts-their-huge-thirst-for-water/

By Brad Reed of Common Dreams

US Secretary of State Marco Rubio has drawn an outraged reaction from critics after he unveiled sanctions against Tomoko Akane, president of the International Criminal Court.

In announcing the sanctions, Rubio described the ICC as “a corrupt and fatally politicised” institution that has allegedly “abused its authority and exceeded its mandate”.

Rubio said the ICC “has repeatedly attempted to assert authority over… the United States and other countries that have not consented to its jurisdiction,” which he said “sets a dangerous precedent for all nations”.

• READ MORE: It won’t be Trump who kills the ICC — it would be the court’s alleged supporters who refuse to take action to defend it
• US sanctions international criminal court president and prosecutor
• Other ICC reports

In addition to sanctioning Akane, Rubio said the US would also be sanctioning ICC senior trial lawyer Abdoulaye Saye.

While Rubio did not mention what specific actions the ICC had taken to warrant such sanctions, a report in The Guardian noted that Saye was charged with investigating war crimes allegedly committed by Israel during its years-long assault on Gaza.

The Trump administration had previously sanctioned then-ICC prosecutor Karim Khan — who obtained arrest warrants for Israeli Prime Minister Benjamin Netanyahu and former Israeli Defence Minister Yoav Gallant — and an additional eight ICC officials.

Israeli officials warned local media in May that the ICC may seek five more arrest warrants for ministers and senior military officials involved in violence against Palestinians in the West Bank.

US protecting Netanyahu
The social media account of Drop Site News accused Rubio of attacking the court solely to protect Netanyahu from war crimes prosecution, pointing out that “there is currently not a single American citizen under indictment, investigation, or facing an arrest warrant” from the ICC.

The sanctions earned the praise of the Israeli American Public Affairs Committee (AIPAC), which accused the ICC of conducting “politically motivated investigations and prosecutions targeting America and Israel”.

Rutgers Law School professor Adil Haque said the ICC sanctions showed the US was acting like a “rogue government” and engaged in “deranged behaviour”.

Michael Karanicolas, law professor at Dalhousie University, did not just point fingers at the US over the sanctions, but at US allies who have sat back as international institutions have come under assault.

When you are shameless, contemptuous of international law and morally bankrupt, you boast of disgrace as though it were an achievement.https://t.co/AHdY7iwyE3

— Elijah J. Magnier (@ejmalrai) August 18, 2026

“I’m no longer surprised by the Trump administration’s efforts to destroy the ICC,” wrote Karanicolas. “What I keep waiting for is any response from Canada or the European governments who ostensibly still believe in international law to defend these institutions.”

War correspondent and international political analyst Elijah Magnier delivered a withering rebuke of Rubio’s announcement.

“When you are shameless, contemptuous of international law, and morally bankrupt,” wrote Magnier, “you boast of disgrace as though it were an achievement”.

Brad Reed is a staff writer for Common Dreams. This article is republished under Creative Commons.

Original source: https://analysis1.mil-osi.com/2026/08/19/deranged-behaviour-marco-rubio-slammed-for-us-sanctions-on-icc-president/

Mention psychotherapy and many people picture a familiar movie scene: someone lying on a couch, talking about their childhood, while a therapist sits behind them taking notes.

That image comes largely from psychoanalysis. But it doesn’t represent most psychotherapy today. Psychotherapy and psychoanalysis aren’t really competing treatments.

Psychotherapy is the broader term for a range of psychological treatments designed to help people change troubling thoughts, feelings, behaviours, or relationships. These include cognitive behavioural therapy, family therapy, and psychoanalysis, which played an important role in the field’s development.

Where did psychoanalysis come from?

Psychoanalysis developed from the work of Austrian neurologist Sigmund Freud in the late 19th and early 20th centuries.

Freud proposed that much of our mental life happens outside our conscious awareness. He believed early experiences and unconscious conflicts could shape how we feel and behave.

Psychoanalysis aimed to bring these influences into awareness. Patients were encouraged to say whatever came to mind, a technique known as free association, while the analyst helped them look for patterns and meanings. Dreams and the patient’s relationship with the analyst could also be explored.

Psychoanalysis also strongly influenced early psychotherapy with children. Freud famously worked with a five-year-old boy known as “Little Hans” for a phobia of horses. His daughter, Anna Freud, later became a pioneer of child psychoanalysis.

So, do you really lie on a couch?

In traditional psychoanalysis, quite possibly.

Traditional psychoanalysis is intensive. Patients may attend several sessions each week, often for years, typically lying on a couch with the analyst out of view.

In the early days of psychoanalysis, the analyst wasn’t necessarily silent. But rather than giving advice or teaching skills, they listened, asked questions, and offered interpretations intended to help the patient recognise patterns outside their awareness. Some psychoanalysts still practise this way.

But from the early 20th century, psychotherapy began to diversify. Behavioural therapies focused on learning and changing what people do. Later, cognitive therapies focused on changing patterns of thinking, while family therapies focused on interactions between people.

Meanwhile, modern psychodynamic therapy grew from psychoanalysis and retains some of its focus on unconscious processes and relationship patterns. But it is usually less intensive. Therapist and client generally sit face-to-face, meet less frequently, and the therapist may take a more active role.

What does psychotherapy look like today?

There’s no single answer because psychotherapy now includes many approaches. Many involve more than just talking.

A therapist might use carefully chosen questions to help someone examine a belief. For example, what evidence supports it? Is there another way of looking at the situation? They might then test that belief with a behavioural experiment in everyday life.

Someone who thinks, “If I speak at a meeting, I’ll make a fool of myself”, for example, might make a small contribution and observe how others respond – perhaps finding things don’t go as badly as they feared.

A therapist might help a person with depression plan activities that provide pleasure or a sense of achievement. Therapy may also involve practising new skills between sessions.

None of this involves mind-reading. Good psychotherapy should be collaborative: therapist and client agree on what they are trying to change and work together towards those goals.

What about children?

Psychotherapy doesn’t always involve an adult talking one-to-one with a therapist.

With children, the person doing much of the therapeutic work may sometimes be the parent.

Evidence-based parenting programs can help parents encourage positive behaviour, respond differently to problem behaviour and strengthen their relationship with their child. Other therapies involve both children and parents or use developmentally appropriate activities.

So, what happens in psychotherapy depends on who needs help and what problem they are experiencing.

But does psychotherapy work?

For many mental health problems, yes. But “psychotherapy” isn’t a single treatment. Asking whether psychotherapy works is a little like asking whether medicine works: which treatment, for which problem?

Some therapies have much stronger evidence for particular problems than others. The Australian Psychological Society’s ⁠Evidence-based Psychological Interventions review brings together research on psychological treatments for a range of mental health problems in adults, children, and adolescents.

So someone seeking help can ask not only, “Does therapy work?”, but “Which therapies have the best evidence for my problem?” They can also ask a psychologist what approach they use and how they’ll know if it’s working.

What about psychoanalysis? Research supports shorter psychodynamic therapies, which grew from the psychoanalytic tradition, for some mental health problems. But traditional, intensive psychoanalysis itself has been studied much less extensively.

So, what’s the difference?

The simplest answer is that psychotherapy is the umbrella, while psychoanalysis is one approach underneath it.

Psychoanalysis played a major role in psychotherapy’s history. But the field has changed enormously since Freud’s time.

Today, psychotherapy is unlikely to involve lying on a couch while someone silently analyses your unconscious mind. It’s much more likely to involve therapist and client agreeing on what needs to change and actively working together.

Trevor Mazzucchelli is a registered psychologist with the Psychology Board of Australia, with endorsement in clinical psychology, and is a member of the Australian Psychological Society and a Fellow of the APS College of Clinical Psychologists. He is a co-author of the Stepping Stones Triple P – Positive Parenting Program and a consultant to Triple P International. The Parenting and Family Support Centre receives funding from royalties generated by Triple P resources, which are developed and owned by The University of Queensland. Royalties are distributed to the University, its Faculty of Health and Behavioural Sciences, and contributing authors. Trevor Mazzucchelli does not hold shares in Triple P International but has received, and may receive in the future, royalties and consultancy fees. Triple P International had no involvement in this article. He has received research funding from the Australian Research Council and the National Health and Medical Research Council. He does not endorse any specific program, product or organisation in this article.

Original source: https://analysis1.mil-osi.com/2026/08/19/whats-the-difference-between-psychotherapy-and-psychoanalysis/

If you’re a keen angler and regularly catch yellowtail kingfish (Seriola lalandi), you may have encountered instances of the fish’s flesh turning mushy and unpalatable once cooked. Some have said it’s a bit like trying to eat wet newspaper.

This can be disappointing for the angler and for the hungry guests you’ve invited to dinner. It is also a waste of food – and of the fish’s life.

It’s often hard to pinpoint which fish will turn mushy when cooked as they can look, smell and taste the same when raw, such as when served as sashimi. The issue only appears once the fish is cooked or prepared as ceviche. In other words, you need heat or a pH change for the problem to reveal itself.

The mushy flesh problem is associated with the parasite Unicapsula seriolae but it’s too soon to say it’s definitely the cause – that research hasn’t been done yet.

What we do know is that this parasite shows up in a lot of kingfish that turn mushy when cooked.

Now, our research suggests infection with Unicapsula seriolae may be more widespread than first thought.

In fact, we found even flesh that doesn’t go mushy after cooking can still be infected.

Mushy kingfish and parasite infection in NSW

I’ve been fascinated by the mushy kingfish problem for over 15 years now, following my own encounters in the waters off Sydney, the mid north coast of New South Wales, and at the dinner table.

There are reports dating back to the 1990s of people in NSW catching kingfish that later turns mushy when cooked.

But our research wanted to go beyond anecdotal evidence, so we scientifically tested for and detected this parasite in kingfish caught in NSW. For the first time, we show it is present in kingfish from NSW.

Scientists had previously only tested for and detected this parasite in kingfish caught in Queensland and Western Australia, and in aquaculture settings in Western Australia.

Our recent research involved working closely with NSW-based anglers, spearfishers and charter operators to obtain donations of kingfish tissue in 2024 and 2025.

We tested for the presence of microscopic parasites in kingfish by:

• looking at samples under the microscope

• doing polymerase chain reaction (PCR) testing and

• DNA sequencing.

In the samples we tested, we found 38.9% of fish were infected with the myxosporean parasite, Unicapsula seriolae. This falls within the range of infections in wild kingfish in previous Australian reports, which put the figure at between at 29.3% and 80%.

But crucially, we also showed that a larger proportion of infected fish (71.4%) did not develop mushy flesh when cooked, or were asymptomatic.

This indicates that relying on fish turning mushy once cooked will mean most infections get missed.

This microscopic marine parasite is similar in size to the human red blood cell, making it impossible to spot with the naked eye. Only some fish will develop signs of infection in the form of black cysts that can be seen with the naked eye.

We also found the parasite in fish caught throughout the year, even though the issue of flesh turning mushy when cooked only usually shows up in fish caught in summer and spring.

This might suggest there’s a temperature link that causes this issue to become more prevalent in warmer months, but it’s too soon to say for sure.

How does this parasite affect fish health and human health?

Scientists don’t yet fully understand the impacts this parasite can have on the health of kingfish. But we do know that in aquaculture settings outside of NSW this parasite has been associated with mass-mortality events (when other pathogens are also present).

Despite this, we still do not know the exact role the parasite plays in the health and physiology of wild kingfish.

Scientists are also unsure what the consumption of raw Unicapsula seriolae-infected kingfish means for human health.

Currently, there is no evidence to suggest it causes food-borne illnesses here in Australia. In Japan, the same parasite has been linked to cases of food-borne illnesses in people who ate raw greater amberjack (Seriola dumerili) – a species closely related to the yellowtail kingfish.

We do not have any accounts of similar cases here in Australia. More research is required to investigate the impacts to human health, if there are any.

All in all, we found most infected kingfish from NSW waters do not become mushy when cooked. And we still do not exactly know why the flesh turns mushy when cooked for some infected kingfish but not others.

Further research is required to understand the significance and consequence of infection, and to develop improved rapid detection methods.

Although sample sizes were small, we are continuing our work to better understand infection.

If you’re an angler or consumer and would like to contribute to the ongoing research you can become a citizen scientist and log your encounters with mushy kingfish on the mushy fish app.

The research upon which this story is based was supported by the Charles Sturt University Faculty Early Career Researcher scheme, the Gulbali Institute and the SUNY Center for Biotechnology and Biodiversity.

Original source: https://analysis1.mil-osi.com/2026/08/19/parasite-linked-to-mushy-fish-could-be-much-more-widespread-than-thought/

Imagine your team has been tasked to deliver a high-stakes policy paper under intense time pressure. Everyone turns to generative artificial intelligence (AI) and within minutes, it delivers a full draft complete with structured arguments and authoritative-looking citations.

The team then transfers the AI-generated text into the corporate template, polishes the narrative flow and structure, and gives it one final check before sending it up the chain. Leadership takes a glimpse of the polished-looking draft, and – assuming the underlying research has already been verified – signs off and publishes.

This scenario is not hypothetical. South Africa’s Draft National AI Policy had to be withdrawn earlier this year after reviewers found a number of the citations pointed to journal articles and authors that didn’t exist. Ironically, the mistakes were introduced by AI.

Similarly, last year, consultancy firm Deloitte provided a partial refund to the Australian government after admitting that generative AI had been used to help produce a commissioned document which contained fabricated citations and referenced quotes from sources that simply did not exist. The report cost Australian taxpayers A$440,000.

But the cost of letting AI do our work and thinking also comes at a cost to our brain.

Value of thought

As seen in the examples above, work tasks produced by AI often look polished on the surface but carry significant underlying flaws. This is a byproduct of how the modern workplace has conditioned us to prioritise the final deliverable – the report, the presentation, the assignment – over the actual process.

However, this strictly outcome-driven approach rarely rewards the process by which results are obtained. When we skip the process, we lose the ability to fully grasp what the output actually means.

This will inevitably result in us finding it challenging to critically evaluate the outcomes we consume. More importantly, it will also impact our capacity to discern facts from partial truths and lies.

Ultimately, the true value of knowledge work lies not just in the final deliverable, but in the clarity of thought and the messy, iterative process required to achieve it.

Blindly relying on AI

Recent empirical evidence also highlights a severe disconnect between perceived benefits of using AI and its improvement in quality.

A 2025 study by the University of Melbourne and accounting firm KPMG surveyed more than 48,000 respondents across 47 countries. It found two in three people (66%) use AI on a regular basis, while more than half believe their performance benefits from its usage.

The study specified that the primary driver of AI adoption was the fear of missing out (48%), leading users to prioritise the speed of delivery over the quality of the final output.

Yet 61% of respondents said they have had no AI training, while 60% also reported inappropriate, complacent and non-transparent use of AI in their workplace.

A separate study from July 2026 by the Centre of AI Safety, a San Francisco-based nonprofit research organisation, found even the top-performing AI agents failed to complete roughly 85% of projects to a standard acceptable for commissioned work.

This indicates that even today’s best AI models still fall short of professional quality on most projects. It also shows we are relying on AI to generate final deliverables without understanding how it gets there.

This blind reliance strips away quality control, replacing genuine knowledge creation with an ever-increasing volume of automated “work slop”.

Improving cognitive skills

So how do we reverse this trend?

The answer isn’t simply banning or limiting AI in the workplace. Instead, we need to redefine our working relationship with AI and the outcomes it produces by taking a more human-centric approach.

First, organisations must set realistic time frames for deliverables that genuinely allow for people to think rather than focus on doing things quicker. There is a need to actively disconnect from AI tools during the brainstorming and structuring phases so ideas and thoughts are driven by actual human synthesis, not just algorithmic prediction.

Traditional whiteboard sessions – where teams physically map out their initial concepts through markers and pens – is a way this may be facilitated.

Second, we must cultivate a culture that is constructively critical of all outputs, whether generated by humans or AI.

This means actively questioning logic, structure, references and underlying assumptions. When teams are expected to defend the behavioural reasoning and methodology behind their work, it very quickly reveals who actually did the thinking and who merely copy-pasted from an AI agent.

Perhaps creating a critque group can create a safe, comfortable environment where feedback and comments can flow freely.

Technology can assist with certain processes, but it is dangerous to assume it can replace our ability to think critically. This is because the most valuable asset we have isn’t the polished final page – it’s the ebbs and flows of the cognitive process that is required to honestly write and evaluate one.

Jongkil Jay Jeong has previously received funding from the Cyber Security Cooperative Research Centre and Department of Foreign Affairs and Trade, Australia.

Original source: https://analysis1.mil-osi.com/2026/08/19/offloading-work-tasks-to-ai-comes-with-a-cost-to-our-brains/

Erupting or silent, volcanoes have always captured our imagination.

As volcanologists, we have seen how that fascination can collide with rolling news coverage and social media, amplifying false claims, misleading images and long-standing misconceptions about how volcanoes behave. We want to help.

Much more than simply getting the science wrong, misinformation can distort people’s perceptions of hazards and risk and influence how they respond to an eruption.

With this in mind, we sought to better understand which misconceptions about volcanoes are most common and how they take hold and spread.

Our newly published research draws on our own experiences as science communicators, as well as an analysis of hundreds of international media articles published during volcanic events.

By providing a clearer picture, we hope to help journalists communicate volcanic hazards more accurately – and to help everyone from any background recognise misleading information.

When misconceptions become misinformation

Our international study team included experts from universities, volcano observatories, and government agencies, while our media analysis covered 375 articles published between 2017 and 2022.

We identified 18 common misconceptions spanning everything from seismology and volcanic hazards to climate, eruption timescales and misleading images.

Some involved attention-grabbing terms such as the Pacific Ring of Fire, supervolcanoes and mega-tsunamis. Others reflected more basic misunderstandings about how volcanoes work.

Volcanic ash, for instance, is often described as “smoke”. In fact, it consists of tiny fragments of rock, crystals, and volcanic glass, mixed with gases and water vapour in an eruption plume.

Similarly, the term magma “chamber” can suggest a vast underground tank of liquid magma. The reality beneath many volcanoes is more complex, involving networks of molten rock, crystals and gas.

We also examined how misinformation spreads during volcanic events around the world.

During Mt Agung’s 2017 unrest and eruption in Bali, old footage of other volcanoes was presented as current activity, while clouds and gas emissions were wrongly reported as eruptions.

Some reports suggested an eruption could affect the entire island, contributing to tourist cancellations and leaving volcanologists to counter false information.

More recently, a 2025 eruption of Mt Etna in Sicily produced a pyroclastic flow within an area already closed to the public. Videos of tourists outside the affected area moving away were shared as people “fleeing for their lives”, creating the impression Sicily itself was unsafe.

The resulting coverage fuelled public anxiety and tourism concerns, while exaggerating the actual danger.

We also examined misinformation surrounding Yellowstone volcano in the United States, which has often been portrayed as an “overdue” supervolcano capable of destroying humanity.

Documentaries, disaster films, and tabloid media have exaggerated the likelihood and potential impacts of a future eruption, while earthquakes have been portrayed as signs an eruption is imminent. In reality, its last 50 or so eruptions were lava flows with only local impacts.

How to spot volcano misinformation

So, how can people navigate the flood of information online during a volcanic crisis?

We recommend first checking the source. Start with organisations responsible for monitoring and managing the volcano, including volcano observatories, geological agencies, and emergency management authorities.

Scientists and other experts can also provide valuable context, but it is worth considering whether they are speaking within their area of expertise.

When it comes to reporting about what a volcano might do next, it is also important to distinguish between what is possible and what is likely.

Volcano science involves uncertainty and scientists and emergency managers may discuss a range of scenarios because they need to prepare for what is more likely.

But an alarming headline stating a volcano “could” produce a massive eruption or tsunami can easily be written as suggesting that it “will”.

And before sharing images and videos they encounter online, it’s always a good idea to ensure they’re credible – particularly as AI-generated content becomes more common.

Is the image actually from the volcano being discussed? Has the same image appeared elsewhere online with a different location or date?

Even with intensive monitoring, volcanologists cannot always forecast what a volcano will do next. Good communication can, however, help people understand what is happening, what could happen, and what is most likely.

That distinction is more important than we might think. Describing volcanic ash as “just smoke” can cause people to underestimate its hazards, while calling a volcano “overdue” can make an eruption seem inevitable.

Our guide is intended to help journalists and individuals recognise such misconceptions, assess the evidence behind claims, and make better-informed decisions during volcanic events.

For journalists in particular, our message is simple: there’s no need to exaggerate the science. Volcanoes are interesting enough on their own.

The authors acknowledge the contribution to this article of study co-author Sam Poppe, Assistant Professor in Planetary Volcanology at the Space Research Centre of the Polish Academy of Sciences.

The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

Original source: https://analysis1.mil-osi.com/2026/08/19/during-eruptions-misinformation-can-spread-further-than-ash-how-can-we-spot-it/

Last week, the Fair Work Commission ruled Gregory Baker, a computing academic at Macquarie University, should be treated as an ongoing, part-time employee, after the university had earlier declined his request to convert from a casual role.

It was immediately described as a “landmark” decision, the first test of Labor’s “employee choice pathway” reforms passed in 2024.

But the ruling also made headlines for other reasons. Baker represented himself at the tribunal, and has said he won with the help of trained artificial intelligence (AI) agents. His success again has us asking: can AI replace lawyers?

On closer scrutiny, Baker’s case looks less like evidence of AI replacing lawyers and more like a powerful illustration of how a highly capable user can employ AI tools to terrific effect.

Request denied

Speaking to the Australian Financial Review following the ruling, Baker said it was actually an AI tool that alerted him to the possibility of converting his role from casual to permanent part-time in the first place.

He had been teaching computer science at Macquarie University over consecutive semesters from 2023 to 2025, and in November 2025, he gave the university the required notice that he believed his work no longer met the requirements of casual employment.

The university did not accept this notification and Baker lodged a dispute at the Fair Work Commission – without a lawyer – in December 2025. The parties could not reach agreement, and the case went to arbitration on May 12. A decision was handed down last Wednesday.

Expert use of AI

Baker has said he won by using multiple paid AI agents, such as OpenAI’s paid offering, ChatGPT Pro. This “team” helped assemble his case, follow up references and anticipate his employer’s counterarguments.

His victory has been celebrated as historic, with the Australian Financial Review describing it as:

the first known successful use of technology by a self-represented person in the legal arena.

However, a few things set this particular case apart. Baker’s IT background, expertise managing AI agents and ability to optimise their use for his case represent a rare level of expertise in using AI in a legal context.

Details included in the Fair Work Commission’s decision also suggest he kept his legal argument narrowly focused on teaching he’d done in one particular unit.

Less expert use of AI in court often sees those bringing claims produce “kitchen sink”-style arguments, which include weak, exaggerated and nonsense claims.

Baker’s dispute was also narrow, limited to the application of a casual conversion law that had not yet been tested. Importantly, the Fair Work Commission (a tribunal, not a court) is designed to be user-friendly, to enable workers to bring claims without a lawyer.

Less positive attention

Elsewhere, the use of generative AI in legal proceedings is attracting a lot of attention for less positive reasons.

Most of this attention centres on the damage caused by inaccuracies, hallucinations and “AI slop”, and how courts and tribunals should best respond.

By making it easier to put a case together, AI has removed traditional access barriers for some litigants. But while case numbers are going up, case precision and quality is going down, making it harder to manage disputes to resolution.

Courts and tribunals are struggling with the volume. At the Fair Work Commission alone, workload has reportedly increased by 70% over three years.

New challenges are emerging as time goes on. Reports suggest litigants and lawyers in some overseas jurisdictions are embedding prompts in digital documents (something called “prompt injection”) to overcome or manipulate AI-based review systems some courts use to process documents.

A big opportunity

Baker’s example shows us something significant: used well, AI tools can empower people with narrow legal disputes and digital skills to achieve successful resolutions, at low cost.

This is an important development in access to justice. In Australia, there is a huge gap between the number of people with legal problems and the very limited funding available for legal assistance.

Most of the community is in the “missing middle”, unable to afford private legal assistance but on incomes too high to qualify for free legal aid.

AI tools stand a good chance of helping people with sufficient legal capability with problems and cases – like Gregory Baker’s – that are a good fit for the solutions AI can offer. These are few and far between, however.

Where might things be headed?

We should expect case numbers and self-representation in courts and tribunals will continue to grow and expand beyond Fair Work.

While there will be some baseless cases, the growth also represents the natural consequence of removing one traditional access barrier to our formal justice institutions – getting in the front door to start proceedings.

The bigger picture challenges are persistent, and raise important questions: who will most benefit from the capacity of AI tools to enhance access to justice, and who will continue to struggle to get basic legal problems resolved?

For courts and tribunals, the challenge will be striking a balance between managing caseloads and delivering justice, while not wasting the opportunity to expand access to justice.

Professor Genevieve Grant receives funding from the Australian Research Council and the National Health and Medical Research Council.

Original source: https://analysis1.mil-osi.com/2026/08/18/an-ai-legal-team-has-won-its-first-case-its-a-rare-win-for-access-to-justice/

Concerns about students cheating with AI are rife within schools and universities.

Some lecturers have even expressed fears students have been “lobotomised by AI” amid reports AI-detection tools are patchy and far from fool-proof. Last week, New South Wales moved to ban take-home assignments for Year 12 to try to stop AI use in assessments.

At the same time, a major generative AI system has developed a way to “watermark” whether or not a text is AI. Developer Anthropic, has announced the text output of Claude (one of the five big AI tools) will include a hidden label or watermark indicating the text is AI-generated.

Files generated by Claude, including Word and PowerPoint, will also contain an additional watermark hidden in the file itself.

What does this mean for efforts to stamp out cheating?

Read more:
Oral exams are making a comeback to stop AI cheating. But they have their own problems

Why a watermark?

Anthropic took this step after signing up to the European Union’s new AI act. The act requires providers of AI systems that create synthetic audio, video, images or text to identify the outputs as AI-generated.

Other tech companies, such as OpenAI (which runs ChatGPT), have developed a text watermarking tool, but not rolled it out publicly.

The watermarking is not an image superimposed over the text. It is invisibly embedded through the text. Readers will need a “detection mechanism” (yet to be released by Anthropic) to pick up the watermark.

If this sounds too good to be true, that’s because it is.

There are still loopholes

As with AI detection, watermarking can be evaded using other AI tools. The absence of a marker also doesn’t guarantee that AI wasn’t used.

Even Anthropic warns their watermarking cannot tell “whether the text was human-written”. It doesn’t work on small samples of writing. It cannot tell whether AI was used to proofread the work, suggest edits or make improvements. It cannot tell whether a different AI system, such as DeepSeek, wrote the text.

OpenAI has reportedly withheld its text watermarking tool, in part, due to concerns it could negatively impact certain groups, including non-native English speakers.

AI use has been normalised

We know AI use is now extremely common among students. The tools are readily available and free to use.

A 2026 UK survey found 95% of undergraduate students use AI, with 94% using it in their assessments.

Unauthorised AI use is most common in written assessments completed outside of class. Educators have been using AI detection software to try and screen work for cheating.

A July report from software company Turnitin revealed more than 53% of submissions from Australian uni students run through its system used some form of AI.

However, an increasing number of institutions have abandoned this technology amid concerns about accuracy, bias, procedural fairness, and successful legal challenges from students overseas.

What do we need to do instead?

So it would be unwise for teachers or institutions to rely on watermarking or detection tools to guard against cheating. These technologies can provide a sense of false security. But where does that leave us?

Some Australian educators are warning “detection-based approaches to integrity are not the way forward”. This is because the focus is on policing whether AI was used, rather than on what the student has learned.

Or as University of Queensland educational psychology expert Jason Lodge has also argued, we need to:

stop looking for evidence that students are using these tools to cheat and shift our emphasis to looking for evidence that learning has occurred.

What happens now?

Advice on alternatives to the “detect and punish” approach has been steadily growing over the past three years. Some of this is via the higher education regulator. It notes:

• we need to prepare students to engage actively, responsibly and ethically in a society where AI is everywhere

• understanding what students have learned requires different types of assessment that are fair, inclusive and suited to the situation.

Concerns about cheating and AI-use have also resulted in calls to return to in-person assessments. But these aren’t workable for those who study fully online. They also don’t prepare students to engage with AI.

The higher education regulator has also outlined how assessments could change in practice. This includes assessments that are connected and build upon each other throughout a whole degree, increasing in complexity. It also includes at least one “secure” assessment task in every unit or subject across a degree program.

Secure tasks can include oral presentations, in-class tasks and supervised practical demonstrations. They must also include mechanisms that verify the person completing the assessment is the enrolled student.

Mark A. Bassett does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Original source: https://analysis1.mil-osi.com/2026/08/18/does-ai-watermarking-mean-the-party-is-over-for-cheating-students/

As students return to Canadian colleges and universities soon, many will enter classrooms where generative artificial intelligence (GenAI) is already part of everyday academic life. Many might use tools such as ChatGPT and Gemini to brainstorm, summarize readings, revise writing and prepare job applications.

The pressing question is no longer whether students will use AI. It’s what universities will teach them about using it with judgment and accountability.

This question has gained urgency since Canada’s government launched its new national artificial intelligence strategy, AI for All. The strategy connects AI with public trust, economic opportunity and Canadian sovereignty. It also includes a National AI Literacy Initiative aimed at post-secondary students and educators.

However, learning to use an AI system is not the same as becoming AI literate. Students need more than the ability to write effective prompts or complete tasks faster. They need accountable AI literacy: the ability to explain why they used AI, assess what it produced and take responsibility for the resulting work.

I have taught professional communication and worked on curriculum innovation that connects classroom learning with changing professional practices. My recent research on prompt engineering has led me to a clear conclusion: teaching students how to communicate with AI can be valuable, but only when it develops rather than displaces human judgment.

Good prompts don’t guarantee good answers

Prompt engineering refers to how we devise and revise instructions to obtain a useful response from GenAI. A strong prompt may identify the task, audience, context, relevant evidence and desired format. These are familiar principles in communication education. Someone writing a policy brief, news release or workplace report needs to consider the audience and purpose before deciding what to say and how to say it.

Prompting can be a legitimate part of learning. A systematic review of prompt engineering in higher education found that structured prompting can support learning when students deliberately formulate, test and refine their interactions with AI. Other researchers describe prompt design as a rhetorical process shaped by purpose, context and audience, rather than a set of technical tricks.

Instructors might, for example, ask students to compare several prompts, explain why they revised them and evaluate how those changes affected the results. Used this way, prompting makes parts of a student’s reasoning visible.

Yet a polished response can still be false, biased or poorly supported. GenAI systems can produce falsehoods with unwarranted confidence. They may fabricate sources, obscure uncertainty or generate claims that students lack the subject knowledge to assess.

Therefore, we should be cautious about relying on AI systems as dependable tutors or collaborators. These systems can produce confident answers without bearing responsibility for whether those answers are true.

That responsibility remains with the person who uses the material.

What accountability requires

Calling for “accountable” AI use raises an obvious question: accountable to whom and for what?

Students are answerable to the people who read, evaluate or rely on their work. These include instructors, classmates, employers, clients and members of the public. In professional settings, AI-assisted communication may also affect patients, employees, customers or communities that had no role in deciding whether AI would be used.

Responsibility begins with factual accuracy, but it does not end there. Students should be able to explain how they used AI in an assignment, how the AI’s claims were checked, whether confidential information was entered and why the final work meets the standards of the course or profession.

UNESCO’s AI competency framework for students combines practical knowledge with ethics, human agency and responsible citizenship.

Students need enough knowledge of a subject to recognize weak or misleading outputs. Consequential claims require verification rather than acceptance simply because they sound authoritative. When disclosure is required, students also need to identify AI’s contribution while remaining clearly responsible for the final work.

AI literacy also includes knowing when not to use AI. Entering private information into a commercial platform, outsourcing high-stakes decisions or automating an assignment designed to develop a foundational skill may be inappropriate even when the technology is capable of doing so.

Students should not carry the burden alone

Accountability should not mean transferring every risk to individual students. Universities have a role in setting clear expectations and designing assessments that make students’ reasoning visible.

Instead of relying mainly on AI-detection software, instructors can also ask students to document revisions, defend their choices and reflect on where AI did and did not improve their work.

One pilot study on incorporating GenAI into educational assessments recommends instructors distinguish between tasks in which AI is prohibited, permitted or actively incorporated. This gives students clearer guidance than either blanket bans or vague permission.

Universities, colleges and schools need to consider which platforms they bring into classrooms. Requiring students to use a commercial AI product may expose their data and deepen universities’ dependence on private technology providers.

AI companies, in turn, remain responsible for system design, data practices and claims about what their products can reliably do. Teaching students to verify outputs does not absolve technology providers of responsibility for faulty systems.

Communication education belongs at the centre

Canada’s AI literacy agenda therefore extends beyond computer science programs, workplace training and tutorials produced by technology companies.

Communication and other humanities and social sciences programs already teach students to ask who is speaking, what evidence supports a claim, which audience is being addressed, what has been omitted and whose interests are served. These questions are central to evaluating AI-generated material.

They also matter in the workplace. An OECD study of changing skill demands in Canada found that occupations highly exposed to AI increasingly require communication, social, language and digital skills.

Preparing students for AI-shaped workplaces does not mean replacing these capacities with technical training. It means teaching students how to apply them when AI becomes part of the work.

Canada needs graduates who can use AI effectively. It also needs people who can challenge unreliable outputs, defend their decisions and recognize when automation is the wrong choice.

If the AI for All strategy is to build public trust rather than simply accelerate adoption, Canadian students will need more than technical fluency. They will need judgment to decide which answers to accept, which systems to question and when AI should not be used at all.

Sibo Chen receives funding from Toronto Metropolitan University and the Social Sciences and Humanities Research Council of Canada. He is affiliated with Canadian Communication Association, International Environmental Communication Association, and the Environment, Science, and Risk Communication Section of International Association for Media and Communication Research.

Original source: https://analysis1.mil-osi.com/2026/08/18/students-need-more-than-ai-skills-they-need-accountable-ai-literacy/

The Canadian government recently introduced AI for All, a national artificial intelligence strategy that has two focuses: building trust and creating opportunity.

As Canada invests in AI literacy and adoption, schools and post-secondary institutions can play a central role in ensuring that future generations are prepared to discern appropriate, effective and ethical uses of the technology.

Over the past year and a half, as doctoral students in education, we have worked with more than 800 educators from schools, universities and professional learning settings to explore how they understand, discuss and respond to generative AI. Our experience with educators led to the development of the GenAI-dentity Survey and Workshop project, designed to help educators reflect on:

• Their comfort with genAI in day-to-day use;
• Assessment and educational practices;
• Their fears and challenges.

So far, more than 800 educators have filled out our survey — whether through our guided workshop or on their own through by completing the module on our website.

Our forthcoming research describes the development of this survey and workshop in more detail, and reports on what we’ve learned so far from surveys and in workshops we conduct.

This book chapter, “The Generative Artificial Intelligence (genAI) Comfort Spectrum: Navigating the Role of genAI Through Self-Assessment to Support Educator Growth,” is part of the Handbook of Theory and Research in Digital Media and Education.

As students leading this project, we’re grateful that it’s been supported by our academic mentors and professors: Saad Chahine and Pamela Beach here at Queen’s University.

Educators feel unprepared, unsupported

We work with educators to engage early adopters and also to engage those who look the other way in discussions about the changing educational landscape. We seek to build trust and create opportunities for discussions on AI use in educational settings.

This matters because educators are on the front lines, from elementary schools to post-secondary institutions. They are encountering genAI as both an opportunity and a disruption.

While genAI has shown to support efficiency and open up new possibilities for education, it raises questions about academic integrity, equity, privacy, bias, environmental impact, dependence on large technology companies and the nature of education itself.

Read more:
Cyberattack affecting school boards spotlights the need for better EdTech regulation in Ontario and beyond

This tension sits at the centre of our research on AI comfort in educational settings.

To date, we’ve found that while educators play an important role in Canada’s AI future, many of them feel unprepared, unsupported and uncomfortable with the new technology. This can lead to them ignoring genAI, rather than mentoring students on when and how to use it.

Comfort is not always easy to name

In developing the survey and workshop, we found that educators do not fall neatly into simple categories of being for or against genAI. This is why we asked educators about their comfort levels with AI.

Comfort with genAI differs from comfort with many other educational technologies because it spans technical, ethical, pedagogical, professional and emotional questions. For example, an educator may know how to use genAI but still feel unsure about whether it aligns with their teaching values, professional responsibilities or institutional expectations.

Discomfort should not be dismissed as resistance or AI illiteracy. Instead, it can signal important discernment and deliberation related to teaching and the meaning of education.

We also learned that not all educators have felt comfortable completing the survey, and that’s OK.

Some educators may attend a workshop, listen carefully and take part in discussion, but still feel uneasy identifying themselves on an AI comfort spectrum.

Rather than seeing this as non-participation, we see it as an important part of the conversation. Some educators may not yet see themselves on the spectrum at all, or may experience their discomfort as too complex to capture through a survey response. The workshop creates space for those educators to discuss hesitation, uncertainty or discomfort without needing to label themselves first.

A tool for reflection

For those who do complete the survey, our project uses four comfort personas, or “sparks:” Traditional Thinker, Puddle Jumper, Curious Explorer and Deep Diver.

These personas are not fixed labels. They function as conversation starters, helping educators reflect on how their comfort with AI may shift depending on context, purpose, role and risk.

For example, an educator may be a Curious Explorer when using AI for their own planning, but a Puddle Jumper when considering student use in graded assignments. The survey helps educators locate their current AI comfort, while the workshop uses those results as a starting point for discussion.

Educator comfort with AI also has direct implications for students. Students are already using genAI, often without structured guidance. If educators feel unable to discuss these tools openly, students may be left to navigate inconsistent expectations on their own, widening inequities in AI literacy when some students receive guidance and others do not.

This is why we are also developing a learner comfort spectrum to sit alongside the educator spectrum. We continue to promote the survey and workshop with educators in different sectors, and are working to bridge our learner-facing survey and workshop to complement the existing educator-facing one.

The two tools are intended to work together: educators can use the educator spectrum to reflect on their own comfort and the learner spectrum to open classroom conversations about students’ AI comfort, discomfort, uncertainty and decision-making. Used together, these can help make AI literacy a shared conversation rather than a one-way lesson about tools.

If Canada’s AI strategy is to build trust in education, AI literacy initiatives need to go beyond technical training and accessing tools. It must encourage educators to reflect on comfort, experiment, ask questions and discuss ethics with peers.

Trust depends on whether educators have the time, language and support to decide when, why and whether AI use is appropriate. Often, individuals who struggle to speak up represent missing voices that are crucial to the conversation. Our workshops suggest that including quieter, more hesitant voices is essential to building trust and creating opportunities as Canada invests in AI literacy adoption across the country.

We hope to continue gathering more insights as we encourage the discussion around comfort and genAI.

The AI-Comfort project has received funding from Queen’s University Faculty of Education: STEAM+ Research Group, and the Center for Community Engagement and Social Change.

The AI-Comfort project has received funding from Queen’s University Faculty of Education: STEAM+ Research Group, and the Center for Community Engagement and Social Change.

Original source: https://analysis1.mil-osi.com/2026/08/18/canadas-ai-strategy-needs-educators-who-feel-prepared-not-just-trained/

This year’s A-level results show a significant increase in the number of students choosing to study politics. In fact, politics saw the largest year-on-year increase of all A-level subjects in the UK, with entries up 11.9% this year compared with 2025.

This is an additional 2,639 students – up from 22,185 to 24,824. Politics still accounts for only 3% of all A-levels studied across the UK in 2026. This increase comes on the back of increases in demand for over a decade. Politics is one of the fastest-growing A-levels – up 81% in the UK since 2010.

Who studies politics?

There is an interesting phenomenon going on in terms of who is choosing to study politics. In 2022, the subject marked an important watershed when, for the first time, more women than men studied politics across the UK. A significant change for a historically male-majority subject.

The trend of a small female majority has continued year on year. In 2026, though, the gender split is almost 50-50.

This change in gender demographics over time is quite distinct from the trajectory of many other social science subjects. Psychology has remained strongly female-majority, while economics and business studies are male-majority subjects.

For young women, there may be several distinct factors at play. First, increased interest may be linked to recent changes to the A-level syllabus. A study found that young women placed greater importance than young men on topics such as human rights, elections and voting, climate change and inequality. These are all topics that feature in the revised 2017 politics syllabus, which covers a wider range of political ideas and global issues than previously.

In addition – while somewhat harder to assess – it seems that social media is playing a role in enabling greater engagement and awareness of political issues and debates among young women. This may be because online debates about issues that resonate with young women, including women’s rights, may be encouraging interest in politics.

This would chime with research showing that traditional classroom discussions are less effective in developing “political self-efficacy” (how much a person believes in their ability to understand and make change through politics) among girls than boys aged 11-16. This suggests that factors outside the classroom, such as social media, may be fuelling increased interest.

Why are teens choosing politics?

According to the British Academy, students taking A-level politics in England tend to come from wealthier families compared with the norm for A-level students. Historically, A-level politics has been a popular, and widely offered, subject at private schools.

Yet recent data shows that the number of state-funded schools in England offering A-level politics has risen. More state-funded sixth forms and secondary schools offering the subject suggest that greater accessibility may be a factor in the rise in uptake.

There are most likely multiple reasons for the rise in demand for the politics A-level over the last 15 years. Factors such as ongoing global conflicts, climate change and the increased volatility of national politics, such as the UK’s six prime ministers in the last decade, have increased interest in and the desire to understand how we are governed and what triggers global political change. The proposed lowering of the voting age to 16 may also have prompted increased interest in the subject among teenagers.

Research suggests that young people care deeply about social and political issues and that they are increasingly keen to understand the systems that affect their lives.

The growing popularity of politics A-level may reflect a generation that recognises that political literacy is no longer simply an advantage, but an essential part of active citizenship. But there is still more work to do to ensure that this growing demand is met. A recent report by the Electoral Commission found that 72% of young people believe schools and colleges should do more to teach politics, democracy and voting.

The challenge for politicians is to turn young people’s growing interest in politics into meaningful political participation. In the last general election only 40% of 18–24-year-olds voted, and just 20% of those were from lower socioeconomic groups. Parties that engage young people on issues they care about and involve them between elections are likely to be rewarded. Ultimately, politicians must move from speaking to young people to working with them.

Young people are not disengaged from politics but increasingly interested in understanding it. The challenge is to build on this interest, to create opportunities for young people from all backgrounds to pursue political studies education at school and university. It also means finding more diverse ways to support all young people to engage with and participate in politics.

Rose Gann is affiliated with the Political Studies Association of the UK.

Ana Isabel Nunes does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Original source: https://analysis1.mil-osi.com/2026/08/18/a-level-results-why-politics-is-the-fastest-growing-subject/

On August 14, the hottest day of an exceptionally hot summer, a train derailed near Lewes in East Sussex, England. Three carriages tipped over and two people were seriously injured. The following day, another train derailed in Essex.

We don’t yet know what caused either derailment, and the UK’s Rail Accident Investigation Branch is still gathering evidence. Network Rail – the public body that owns and manages most of Britain’s railway network – said in response that this summer’s repeated heatwaves have “presented exceptional challenges to the railway”. We do know that heat poses particular problems for railways, and that heat-related failure is an increasing risk in the UK as temperatures rise.

Xueyu Geng is a Professor in Ground Engineering at the University of Warwick, and an expert in railway track design and embankment stability. The Conversation asked her about the key challenges for a rail network in heatwaves, and how trains can stay safe and efficient in ever more extreme heat.

What happens to railways in extreme heat?

Steel expands when heated. Most of the network uses continuously welded rail: long lengths that are effectively restrained by the sleepers, fastenings and ballast, rather than jointed sections that can expand freely. When the rail heats up it wants to get longer but can’t, so it builds up substantial compressive stress instead.

If that stress exceeds the track’s resistance to sideways movement, the track can shift sideways, causing a thermal buckle. That’s why extreme heat is a genuine safety issue, not just a comfort-and-delays problem.

Rail temperature and air temperature are also two different things. Direct sunshine matters a lot: a stretch of track in full sun can run considerably hotter than a shaded section experiencing the same air temperature.

Temperature is only half the story, though. Ballast – the layer of crushed stones beneath the tracks – along with sleepers, fastenings and the condition of the track and ground all affect how much sideways movement it can withstand.

So there’s an important distinction between a “hot rail” and an “unstable track”. A railway can run a high rail temperature safely if the track system as a whole has enough resistance in reserve.

Is this a particular problem for Britain? Do hotter countries manage it better?

I wouldn’t say British infrastructure was simply designed for the “wrong” temperature; British engineers have always had to design for thermal expansion and contraction. The more fundamental issue is that the climate is shifting during the working life of the infrastructure.

Historically, the extreme temperatures built into design and operational planning reflected a climate where very high temperatures were rare. We’re now seeing heatwaves that are more frequent and that push into a temperature range many existing assets simply didn’t experience for most of their service life.

There are already examples of heat causing derailments. For instance, in 2015 a freight train derailed near Langton, Lincolnshire, on the hottest day of the year to that point. An investigation found the track had buckled in the heat.

The risk is now being managed at increasingly high temperatures. On July 19 2022, the UK’s first day over 40°C and still its hottest ever day, operators shut down one of the main lines linking London to northern England and Scotland. The closure was a precaution: rather than risk trains encountering buckled track, operators stopped services during the hottest part of the day.

Extreme heat is especially awkward for a railway specifically, because the infrastructure is so tightly constrained, you can’t let a track drift sideways even slightly, since wheel-rail interaction depends on very tight tolerances. Two adjacent stretches of track exposed to identical weather can have very different resistance to buckling, depending on their stressing history, ballast condition, fastenings, geometry and the ground underneath.

There’s no single “heat-proof” railway technology to copy from hotter countries. What they have is the advantage of treating high heat as a normal design condition rather than an exceptional one, with track designed, maintained and operated accordingly.

Our design assumptions and maintenance strategies need to reflect the climate the railway will actually experience going forward, not the climate it was built for.

How have railways adapted, and what would a truly climate-proof railway involve?

The biggest shift needed is from a “reactive” to a “predictive” approach. At the moment, when extreme heat is forecast, operators step up inspections, monitor rail temperature, and impose speed restrictions where needed. Useful, but it’s responding to immediate risk rather than anticipating it.

The next generation of railway management should bring together weather and solar radiation forecasts, actual rail temperature, track geometry, rail stressing history, ballast and fastening condition, drainage, ground and embankment condition, and train speed and traffic loading. Put those together and you can identify which specific sections of track are vulnerable, rather than treating an entire route as uniformly at risk.

This is where new technologies have a strong role to play. Remote sensors, “digital twins” (computer models of a train system that can be updated with real-world data) and modern data science can all help spot a section approaching a critical condition before it becomes unsafe.

A truly climate-resilient railway isn’t just “stronger rails in the ground”. Prolonged hot, dry weather can change ground moisture and affect earthworks at the same time as thermal stress is building in the rail itself. That’s why resilience has to cover everything from train tracks and drainage to signalling and power systems, all together, not rail steel in isolation.

Should we design new tracks, or retrofit old track, for the heatwaves of the future?

Railway assets have very long lives: a track, bridge or embankment built or substantially renewed today could still be in service in 50 or 100 years. That creates a fundamental mismatch between the current design life and a climate that keeps changing within that lifetime.

The right question isn’t “can this new track cope with today’s hottest day?” It’s “what climate will this asset have to withstand across its whole life?”

This hits railways harder than most other transport modes, for a specific reason: the track has to stay precisely aligned. Roads suffer in heat too – asphalt softens, concrete expansion joints get stressed – but a road vehicle has rubber tyres and steering, giving it some lateral tolerance for minor surface movement. A railway wheel and its raised inner lip – the flange – don’t have that margin. Continuously welded rail has to stay within millimetre-scale geometric tolerance over very long, continuous lengths, with compressive stress building as temperatures rise.

For new infrastructure, climate projections for the middle and later parts of this century should be part of engineering decisions now, because that infrastructure will still be in service then. But I wouldn’t design every component to withstand an extreme 2100 temperature outright: that risks enormous, unnecessary cost for conditions that may not materialise for decades.

The better approach is risk-based adaptation: build appropriate resilience into new infrastructure from the outset, and for the existing network, identify the specific sections where future climate creates unacceptable risk and progressively retrofit those. That might mean improving ballast resistance, drainage, embankment stability, monitoring or thermal management, depending on what the local vulnerability actually is.

Xueyu Geng does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Original source: https://analysis1.mil-osi.com/2026/08/18/can-britains-railways-cope-with-ever-more-extreme-heat-an-expert-qa/

Jason Arday’s death at 41 came only days after his resignation from the University of Cambridge. He had been facing intense scrutiny of his academic record and allegations of plagiarism.

Arday’s story had also become fodder for the ongoing accusation by some critics that universities pursuing racial diversity do so at the risk of compromising academic merit.

Black academics can mourn a relatively young colleague while also anticipating the burden Arday’s case may place on scholars who had no connection to his work but may now find their own qualifications subjected to renewed suspicion.

The Arday controversy becomes more troubling when questions about one appointment widen into claims that Black scholars would largely disappear from elite institutions under a supposedly pure merit system. That argument echoes an enduring anti-Black idea that racial inequality reflects differences in ability rather than unequal institutions.

One of Arday’s most prominent accusers, former Cambridge philosopher Nathan Cofnas, is a longstanding critic of diversity, equity and inclusion (DEI). Cofnas has argued that Black people would be far less represented in elite professions under a system based purely on merit.

That claim comes as Canadian universities pursue Black faculty hiring commitments and amid a renewed debate over the role of equity in academic life.

McGill University set a goal of reaching 85 Black tenure-track or tenured professors by 2032. The University of British Columbia plans to recruit up to 23 scholars by 2028. My colleagues and I are studying these initiatives and their implications for Black faculty and Canadian universities.

Read more:
Reckoning and resistance: The future of Black hiring commitments on campus

Recent editorial commentary on Canadian higher education has argued both for and against considering DEI in admissions and hiring.

The debate over DEI has also reached Parliament, where it has shaped discussions about funding federal research. Black faculty are often made the public face of DEI. When controversy follows, they can also become evidence of its supposed failure.

‘Black excellence fatigue’

The language of “Black excellence” carries a heavier burden than celebration alone suggests.

As McMaster political scientist Nathan Andrews explains, the term highlights the contributions of Black people working in institutions that have “historically underappreciated their efforts and contributions.”

However, he continues, “the idea itself can be exhausting and even harmful to the well-being of Black people.” He describes a “Black excellence fatigue,” and argues that Black academics can face pressure to excel because their legitimacy and even their competence may require repeated proof.

Research shows that Black professors’ authority can be questioned in classrooms and that racialized faculty face disadvantages in promotion and tenure.

Universities can intensify that burden by presenting Black appointments as institutional milestones. A scholar becomes the first in a faculty, the youngest in a particular post or the only Black professor in a field. Arday was repeatedly celebrated as Cambridge’s youngest Black professor.

When an appointment symbolizes racial progress, serious allegations can spread suspicion beyond the individual. Black academics have already warned that Arday’s alleged failures will be used to question Black academic legitimacy and diversity initiatives more broadly.

Unequal treatment

Academic integrity scandals have not always produced such expansive consequences. Some cases point to a double standard when it comes to institutional responses.

In 2004, Harvard found that law professor Laurence Tribe, accused of plagiarism, had committed “a significant lapse in proper academic practice,” but imposed no formal discipline. Tribe, a white man, recognized and took responsibility for his failure “to attribute some of the material” in question.

At Western University, where I teach, former psychologist J. Philippe Rushton infamously argued in the 1990s that racial groups differed innately in intelligence and tried to explain those differences through genetics. Though his work and tenure were protected at the time, Western’s psychology department now describes much of his work as racist and scientifically flawed.

Equal standards should not mean lowering the bar. They should leave room for accountability and repair without turning one scholar’s misconduct into a judgment about white intellectual fitness.

These uneven consequences give Arday’s death added weight.

His death follows Claudine Gay’s resignation from Harvard where plagiarism allegations against the former university president became entangled with a broader campaign against DEI. Gay reported receiving racist abuse and death threats.

Read more:
I am not your nice ‘Mammy’: How racist stereotypes still impact women

Meritocracy and systemic barriers

Cofnas has argued that under meritocracy, Black people would largely disappear from high-profile positions outside sports and entertainment, including from university faculties. His claim calls upon a tired racist trope in which Black people are imagined as suited to physical or performative success while intellectual authority remains suspect.

Similarly, Donald Trump’s reference to “Black jobs” in 2024 drew criticism for trading on a related assumption about the work Black people are expected to occupy.

Equity measures on campus emerged in response to documented disparities within hiring and promotion. They are imperfect attempts to correct workplace barriers that can give some people an advantage before merit is even considered. In the U.K., 2025 data shows the proportion of professors who are Black remains at one per cent.

Wavering public trust at universities

The Arday controversy lands amid a wider credibility problem within academia. Scientific paper mills publish false research that require mass retractions. Manipulated peer review processes expose weaknesses in systems meant to certify scholarly quality.

If appointment and promotion processes also fail, the relatively strong public confidence Canadian universities enjoy may weaken.

Cambridge’s vice-chancellor described senior appointments as faculty-led when announcing an independent inquiry into Arday’s appointment. Cambridge academics disputed this characterization because Arday’s appointment had also gone through a central university board.

Faculty elsewhere should now watch how institutions respond when reputational damage creates incentives to blame faculty for failures.

The Arday case could become a reason for universities to expand oversight, even though the questions it has raised extend far beyond Black faculty hiring.

Heightened scrutiny may also affect disabled and neurodivergent scholars. Reporting on Arday extended to his autism diagnosis and personal history, raising concerns about how easily disability can become another site of scrutiny in institutions where disabled faculty already report discrimination.

In a more risk-averse university, differences in communication or ways of working could be reread through assumptions about cognitive capacity or the ability to withstand academic pressure.

The wider stakes should concern everyone in academia. Coverage of Arday unfolded within a British media landscape with a documented history of anti-Black bias..

Read more:
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Cambridge academics have questioned the university’s handling of Arday and its response to the media.

Universities need credible standards and fair accountability. They also have a duty to support faculty when scrutiny becomes disproportionate or dehumanizing.

Leaving Black faculty exposed when institutional visibility becomes costly sends a pointed message about how disposable those commitments can be. The research on the effects of racism and isolation in academia remains thin. Arday was among the scholars trying to fill that gap.

Universities cannot remain indefinitely undecided about equity. Inviting people in without being prepared to stand behind the principles that brought them there may be one of the clearest ways to undermine it.

Cornel Grey receives funding from the Social Sciences and Humanities Research Council (SSHRC).

Original source: https://analysis1.mil-osi.com/2026/08/18/jason-ardays-tragic-death-reveals-the-intense-skepticism-black-scholars-face/

The death of Professor Jason Arday has left many of us with a profound sense of sadness. For Black academics in particular, his loss feels deeply personal. Jason represented possibility – a reminder of what could be achieved despite the barriers that continue to shape Black people’s experiences in education.

I had the privilege of knowing Jason and welcoming him to speak to scholars on the Accomplished Study Programme in Research Excellence (Aspire), which was set up to address the underrepresentation of Black students progressing into doctoral education.

Jason had been facing accusations of plagiarism and resigned from his position at the University of Cambridge. He was found dead on August 14 2026.

His passing has made me reflect on an uncomfortable question: do Black academics really belong in UK higher education? Of course, we should belong. The more difficult question is whether our institutions consistently make us feel that we do.

This question has shaped much of my work through the Aspire programme. Across three cohorts, Aspire has supported 59 Black and mixed-Black scholars. Fifteen progressed into fully-funded PhDs at universities across the UK.

Those numbers matter. But our research suggests that the most important impact of such programs cannot be captured through numbers alone.

Despite years of commitments to equality, diversity and inclusion (EDI), Black academics remain significantly underrepresented at the highest levels of higher education. Only around 1% of professors in the UK are Black.

Behind that statistic are people navigating systems in which they may rarely encounter someone who looks like them in positions of seniority.

Scholars were transformed not simply by learning how to write research proposals, navigate PhD applications or identify funding. What mattered enormously was the building of relationships.

They encountered mentors who believed in them. They met Black academics who made becoming a researcher seem possible. They entered spaces where conversations about race did not have to be avoided. They became part of a community in which their ambitions were taken seriously.

For some, the journey towards a PhD began with something much simpler than research training: someone telling them that they belonged there.

Sometimes, exclusion is being the only Black person in the room. Sometimes it is not knowing the unwritten rules. Sometimes it is constantly having to prove that you deserve to occupy the space you have already earned.

And sometimes it is quietly wondering: is this place really for someone like me?

Supporting Black scholars is not an act of charity. Universities are intellectually richer when knowledge is produced by people with different histories, experiences and perspectives. Different questions are asked and assumptions are challenged. Students encounter new possibilities for who can become a professor, researcher or intellectual leader.

This is why the current moment should not lead universities to become less committed to EDI. It should make us more intentional about it.

EDI cannot simply be something written into strategies, displayed on websites or celebrated during particular months. It must be something people experience. And one of the simplest tests of meaningful inclusion is whether people genuinely feel they belong.

My work draws on ubuntu, the African philosophy often captured as “I am because we are”, alongside omoluabi, a Yoruba philosophy concerned with character, integrity and our responsibilities towards others. Both challenge the idea that academic success is purely an individual achievement. None of us flourishes alone.

Read more:
How African principles of community are helping Black students in the UK into PhD study

Most academics can remember somebody who opened a door, encouraged them after a rejection, advocated for them, or simply believed in them before they fully believed in themselves.

That is why racial equity must move beyond access towards belonging and duty of care.

One message consistently ran through Jason’s work and his interactions with others: kindness and love matters. Behind our titles, publications, grants and achievements are human beings who need community, encouragement and care.

There is a difference between being invited into a university and feeling wanted there; between being represented and being valued; between surviving academia and flourishing within it.

Universities therefore need to ask difficult questions. Who mentors Black scholars? Who advocates for them? Who notices when they are struggling? Where can they speak honestly about their experiences? Who reminds them that they do not have to navigate academia alone?

This is not the time to retreat

These questions are becoming more urgent at precisely the moment when EDI initiatives are facing increased scrutiny and universities face enormous financial pressures. There is a danger that universities will respond to this situation by retreating from EDI initiatives.

If mentoring changes trajectories, fund it. If communities create belonging, sustain them. If targeted interventions enable talented Black scholars to enter doctoral education, invest in them.

Otherwise, EDI risks becoming performative – something institutions are comfortable talking about but less willing to resource.

The question should never really have been: do Black academics belong in higher education? We do. The question for universities is: what are we doing to make sure they know that they do?

Jason’s work feels especially important now. Kindness may sound too soft for conversations about structural racism. I believe the opposite. There is something profoundly powerful about creating institutions in which people genuinely care whether others flourish.

At a time when some Black academics are looking towards the future with uncertainty and wondering where they truly belong, retreating from racial equity cannot be our response.

We must instead build universities where Black scholars do not merely enter and survive, but are supported, valued and able to flourish.

Ifedapo Francis Awolowo receives funding from the Office for Students and UKRI.

Original source: https://analysis1.mil-osi.com/2026/08/18/jason-ardays-death-black-academics-need-to-feel-they-belong-in-higher-education/

A couple of weeks ago, Netflix released The Idaho Murders: College Nightmare, a documentary revisiting the 2022 killings of four University of Idaho students — Ethan Chapin (20 years old), Kaylee Goncalves (21), Xana Kernodle (20) and Madison Mogen (21).

The case, which seemed closed when Bryan Kohberger pleaded guilty in 2025 and was sentenced to life in prison, isn’t actually closed. Kohberger has since petitioned the court to withdraw that plea, arguing coercion and bad legal advice. The victims’ families are ready and unfraid to face him in court again.

The three-part series, detailing the grisly mass slaying, is doing exceptionally well. In its first five days on the streamer, it had brought in 18.2 million views and outperformed other Netflix titles.

And this has revivified ongoing questions about why this type of programming is so alluring. What does it say about us as a society when lurid content like this is so central to our cultural milieu?

The psychology of fear

Although true crime feels current, it is the fourth iteration of the genre under one name or another since the mid-19th century. In terms of explaining it’s appeal, no one explanation suffices. It may be best understood as a system of complex processes, with each of the following mechanisms operating simultaneously at different levels: individual psychology, moral reasoning and social belonging.

Foremost among these is a phenomenon known as benign hedonic masochism. Masochistic behaviours, or enjoyment derived from self-suffering, can, in extreme forms, be pathological and even dangerous. But it’s the “benign” part that provides us the intellectual licence to enjoy content about murder and mayhem.

The Idaho Murders: College Nightmare trades on exactly this: dramatized crime-scene reconstruction and survivor interviews, all consumed from the safety of a couch.

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Drawing on Paul Rozin’s pioneering theory of hedonic reversal, benign hedonic masochism describes a paradoxical psychological condition through which people acquire pleasure from simulated aversive experiences that generate fear, anxiety and emotional discomfort, all while remaining fundamentally safe.

The maladaptive and self-deprecating language that often surrounds true crime consumption, such as “binging,” “obsession,” “addiction” and being a true crime “junkie,” reflects a certain self-awareness of this reality.

More than just a voyeuristic exercise, true crime offers controlled encounters with uncertainty, danger and moral disorder, while simultaneously reassuring audiences of closure and finality.

In an era characterized by declining institutional trust and heightened perceptions of crime, the genre functions as an emotionally charged but socially acceptable means of indulging those same anxieties. Psychologists have a name for it: opponent-process theory.

The theory proposes that intense emotional experiences trigger a secondary, opposing emotional response. Dread, tension and even revulsion can give way to relief; anxiety to reassurance. With repeated exposure, the negative reaction may weaken while the positive after-effect, or the high, becomes more predictable and prolonged.

This is likely why true crime is especially prevalent on streaming and podcasting platforms that are built for continuous, immoderate consumption or “binging.”

Didactic storytelling

One of the other recurring explanations for true crime’s enduring popularity has to do with who’s actually consuming it. Adult women, in particular, are consistently identified as the target demographic for cable and streaming true-crime series, and the pattern holds even more strongly in podcasting.

One explanation for that skew is that the genre serves a didactic function: it imparts factual and moral lessons.

Rather than consuming crime narratives solely for entertainment, audiences may be using them as informal lessons in vulnerability, risk and survival. They watch series and listen to podcasts about sadistic psychopaths like Ted Bundy and Richard Ramirez to know how to spot them, how to thwart a possible kidnapping and how to avoid becoming a victim in a way the women in those cases so often couldn’t.

Originally coined by communications professor George Gerbner as “mean world syndrome,” the theory holds that repeated exposure to mediated violence can cultivate heightened perceptions of danger, vulnerability and interpersonal threat. Audiences, in the grip of that same syndrome, may then use crime narratives to gradually build a repertoire of mental scripts regarding warning signs, predatory behaviour and situational risk.

In this respect, consuming true crime resembles a form of low-stakes threat simulation, allowing the rehearsal of responses to hazards they hope to never encounter in real life.

Whether these lessons are empirically valid or merely produce an exaggerated sense of danger remains subject to some debate.

A true crime community

There are certainly other theories, too, about the psychological undergirding of this current true-crime zeitgeist. Some researchers point to morbid curiosity. Others frame it as a form of boredom-induced sensation-seeking or what’s been called negative information seeking (an appetite for unsettling information for its own sake).

And in some cases, the fascination with male offenders in particular has been linked to a disordered erotic interest known as hybristophillia. But the one thing these theories tend to converge on is that true crime, in its current form, provides a sense of community.

The barriers to membership in this community are remarkably low. Listeners can move effortlessly from consumers to contributors through message boards, Facebook groups, TikTok communities and even their own DIY podcasts.

In contrast to traditional media, true crime in its current form invites collective interpretation: armchair sleuthing, theory-crowdsourcing, shared meaning-making. It fosters a participatory culture in which audiences do not merely follow cases; they also actively help retell them.

That the path to community, in an increasingly segmented and partisan reality, includes a road paved by serial killers and cold cases may say less about our fascination with death than it does about our enduring need to find one another through stories.

Michael Arntfield does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Original source: https://analysis1.mil-osi.com/2026/08/18/why-are-we-so-obsessed-with-true-crime-hint-its-not-just-morbid-curiosity/