Category: The Conversation

Source: The Conversation – USA – By Andrea Hickerson, Dean and Professor, School of Journalism and New Media, University of Mississippi

Historically, when the U.S. has undertaken military action against foreign governments, journalists have relied heavily on government sources and rallied “’round the flag,” often uncritically sharing official narratives about U.S involvement. This has been evident during periods of U.S. military engagements in Vietnam, Iraq and Afghanistan.

Recently, however, the Pentagon has restricted access for legacy news organizations. And on March 14, 2026, Brendan Carr, chairman of the Federal Communications Commission, replied to a social media post from President Donald Trump complaining about reporting on U.S. involvement in Iran. Carr threatened to deny license renewals to broadcasters not operating in the “public interest.”

“The People of our Country understand what is happening far better than the Fake News Media!” Trump asserted in his original Truth Social post.

This hostile relationship between journalists and a presidential administration is only part of the story about what is or isn’t happening on the ground in Iran and the Middle East.

In times of conflict, information about military activity can be seen as another domain of conflict, much like air, land and sea. Countries, including Iran, have long tried to manipulate information to persuade or influence what people think outside the region.

A preprint, not yet peer-reviewed study authored by academics affiliated with the U.S. Air Force and the U.S. Air Force Academy describes increased government funding and attention to “cognitive warfare,” or efforts to influence what people think through strategic messaging.

A common call to action from advocacy and educational groups in politicized situations where misinformation weighs heavy is to teach media literacy. Conventional wisdom holds that if people only knew how to read the news and look for bias, they would understand a situation more clearly.

As a journalism scholar and educator, I agree that media literacy is valuable. But it’s also time-consuming. It’s impractical to complete a full training or curriculum when faced with immediate current events. As an abbreviated measure to assess the current Middle East conflict, readers can start with the premise that information is contested and an extension of the battlefield.

Key questions to ask

This assumption reframes news not as something that finds a reader by chance, but as something someone wants a reader to see. It primes readers’ critical thinking.

Then readers can consider some key questions:

Why does the author of this information want me to see this?

The obvious answer is that they think it’s important, but what are they focused on? Military progress? One actor in the conflict? Civilian responses? Public opinion? Diplomacy? Asking these questions helps assess what is left out and helps readers resist the temptation to extrapolate details they can’t know from a single news story.

What information does this person or organization have access to?

Because Iran is inaccessible to many journalists, readers must be especially careful about reporting purporting to know or show what is going on inside Iran. For sure, information is coming out via citizen reports and social media, but it is hard to verify and interpret.

Relatedly, and especially when consuming content from social media, readers can ask:

What about an author’s personal experience may inform their interpretation of events?

Media produced for and by diasporas – people displaced from their country of origin by choice or force – is a good source for contextualized and expert information about conflicts in their country of origin. But diasporas can also be deeply political and strategic in what they share. As a general consumer, readers don’t need to get to the bottom of the veracity of the information they share. Readers can simply be aware of disaporas’ positions so they can factor this into their interpretation and understanding of the conflict.

What do different people or organizations have to gain or lose by people widely seeing specific information?

If information is a battlefield, actors will make strategic choices in what they will share with the public. Sometimes they will shield information from the public or deny information. However, undesirable and unflattering information occasionally gets out and circulated, as was the case when a missile struck an Iranian elementary school.

Politicians will want to show they are winning. Journalists may want to show they are being a watchdog on the government. Readers can consider the goals of both the authors and the sources they cite when trying to orient themselves around the information they share.

Transparent fact-checking

Beyond media literacy, there are several potential short cuts to finding accurate information about immediate events in Iran.

First, readers can look for opinions and commentary from established experts on the Middle East, Iran, oil, the military and other related fields. Too many readers claim expertise after reading a few popular articles or listening to a podcast.

Instead, they can look for people who have been observing and researching the region for years – people whose work has been already validated by peer review. As a starting place, readers can look for subject matter experts on the social network LinkedIn or search for research on Google Scholar. Readers can also see whether authors of older popular books are writing about contemporary events on websites or blogs.

Think tanks that produce research reports may also be helpful, but sometimes think tanks with neutral-sounding names are politically affiliated. A close read of the “About Us” page and perusing the list of funders can offer some helpful clues.

Finally, perhaps the most efficient way to evaluate what is happening in Iran is to follow fact-checking and open-source reporting organizations. These groups often do a better job showing their assessment work and linking to evidence than do traditional news outlets, which focus on narrative structure and a cohesive final product.

Poynter, a nonprofit journalism institute, recently detailed the work of Factnameh, run by an Iranian fact-checker in exile. Bellingcat and Indicator are two excellent open-source reporting organizations that use public data to investigate whether actual events match circulating narratives.

And sometimes traditional news organizations do similar types of investigations, such as this example of The Associated Press debunking video misinformation in Iran.

The transparent methods of fact-checking and open-source sites can also serve as interactive exercises in media literacy. Both Bellingcat and Indicator regularly showcase information validation tools that readers can use.

Regardless of how much effort readers choose to spend on evaluating the accuracy of reporting on Iran, none of us are watching the battle from the sidelines.

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

– ref. Information is a battlefield: 4 questions you can ask to judge the reliability of news reports and social posts about the US-Iran war – https://theconversation.com/information-is-a-battlefield-4-questions-you-can-ask-to-judge-the-reliability-of-news-reports-and-social-posts-about-the-us-iran-war-278384

Source: The Conversation – USA (3) – By Jamie Hartmann-Boyce, Assistant Professor of Health Promotion and Policy, UMass Amherst

Since the Affordable Care Act subsidies expired at the end of 2025, Americans have undoubtedly been encountering a great deal of confusing information surrounding health care costs and insurance plans.

From five-figure deductibles to premiums higher than people’s mortgages, costs are rising across the board.

With this comes difficult decisions around health care plan enrollment. No one can know exactly what their health care needs will be in any given year, so people are forced to hedge their bets in choosing plans.

What plan you pick has a huge impact on what you will end up paying.

However, many Americans don’t understand key health insurance terms. For example, people who’ve completed fewer levels of education and people without health insurance are less likely to understand the jargon. This can get in the way of picking the right plans.

As scholars of health policy, evidence-based health care and health economics, we believe understanding these terms can help you pick what plan might be the best for you.

Frequently encountered health insurance terms

The first of these is your health insurance premium. This is the amount you pay each month for having health insurance coverage, whether or not you use any services. Premiums can be expensive, but they are predictable. Once your premium is set for the year, it won’t change.

What’s much harder to predict is how much of each medical bill you will have to pay yourself, known as out-of-pocket costs. These are sometimes also referred as “patient cost-sharing” or “copays.” These typically come in three forms: deductibles, coinsurance and copayments.

A deductible is how much you need to spend on your health care in a given year before your insurance starts covering any costs. Under plans with a deductible, you pay the full cost of health care services first – essentially as if you did not have health insurance – until your total spending reaches the deductible amount. Once you reach that threshold, your insurance will start paying for your additional medical costs.

But in most plans, even once you hit your deductible, your insurance will still not cover the full cost of your care. You will continue to pay a portion of the bill through coinsurance, which is the percentage of the cost of care that you are responsible for paying. For example, if your coinsurance rate is 20% and you receive care that costs US$500, you would pay $100 (20% of $500).

What often makes coinsurance confusing is that while the coinsurance rate – the percentage – is usually listed on your health insurance card, you still need to know the total cost of your care to calculate how much you will owe. That cost is difficult to know in advance because reliable health care prices are difficult to find and health care needs – and the services required to treat them – can be unpredictable.

Then there are copayments. This is a fixed amount you pay for a health care encounter, such as $20 for a primary care visit or $150 for an emergency department visit. In everyday language, people sometimes use copay to refer to any amount a patient pays out of pocket. Technically, however, a copayment refers only to a fixed fee paid for a health care service.

Whether through deductibles, coinsurance or copayments, these out-of-pocket amounts can add up quickly. To protect patients, especially those who need a lot of care and could otherwise face devastating medical bills, federal regulations require health insurers to limit how much patients can be asked to pay out of pocket each year for covered services.

This amount is called the out-of-pocket maximum. This is sometimes also called the out-of-pocket cap or out-of-pocket limit. Once your total out-of-pocket spending reaches that limit, your insurance must pay 100% of the cost of additional covered services for the rest of the year.

Additional factors to consider

These insurance rules can become even more complicated. Many plans have multiple different deductible amounts, coinsurance rates, copayments and even out-of-pocket maximums, depending on several factors. For example, in family plans, each person may have their own deductible or out-of-pocket maximum, but there may also be thresholds and limits that apply to the family as a whole. Cost-sharing can also vary by the type of care you receive. For instance, inpatient hospital care may be subject to a different set of cost-sharing rules than outpatient care.

Another important factor is whether your health care provider has a contract with your insurance company. Providers who have such a contract are called in-network providers. Those who do not are called out-of-network providers. Some insurance plans further divide in-network providers into tiers.

Providers in Tier 1 are the most preferred by the insurance plan, often because they agreed to provide services at relatively lower prices. Other in-network providers may be placed in Tier 2. Costs to you tend to be lowest for services from Tier 1 providers, higher for services from Tier 2 providers and highest for services from out-of-network providers. Some insurance plans may not cover out-of-network care at all.

There are often trade-offs between these elements – low premiums look great on the face of it, but any money you save by paying lower premiums is often offset by significant out-of-pocket costs, limited options for in-network providers, or both.

The problem, of course, is that it’s impossible to predict how much health care you might need. If you could somehow know you weren’t going to need much health care in the following year, then a low-premium, high-deductible plan would make sense.

If, on the other hand, you knew you were going to receive a catastrophic diagnosis or be in a life-altering car accident, you would want to opt for a plan that might include higher premiums but lower copays.

Gambles and trade-offs

If everyone knew all the medical care they needed could be provided by any general doctor, they might not care much about what or who was in-network. But if they knew they were going to need specialist surgery for a rare type of tumor, for example, offered at only one center out of state, they would want to consider what counts as in-network – or the costs of going out of network – in substantially more detail.

In many other countries, people don’t face the same burden. In nations with universal health coverage, understanding health insurance jargon isn’t a matter of financial survival. Because coverage is guaranteed, people do not have to agonize every year over choosing a health plan based on countless variables.

But until meaningful change comes about in the U.S., the best many Americans can do is understand health insurance jargon so they can choose plans that work best for them.

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Jamie Hartmann-Boyce receives research funding from the National Institutes of Health, the Food and Drug Administration, the Truth Initiative, and Cancer Research UK.

Michal Horný consults to VBID Health. He receives funding from Arnold Ventures and the National Institutes of Health. He received speaker honoraria and/or travel support from Health Care Cost Institute, Georgetown University, Brown University, Charles University (Czech Republic), Masaryk University (Czech Republic), and the Czech Academy of Sciences (Czech Republic).

– ref. Health insurance jargon can be frustrating and confusing – here’s how to navigate it – https://theconversation.com/health-insurance-jargon-can-be-frustrating-and-confusing-heres-how-to-navigate-it-277355

Source: The Conversation – USA (2) – By Andrew Garin, Associate Professor of Economics, Carnegie Mellon University

If you’ve ever ordered food through DoorDash, Uber Eats or Instacart, you may have realized the person who delivers it isn’t a salaried employee. They’re gig workers – independent contractors who pick up delivery tasks through an app, get paid per delivery and have no guaranteed hours, benefits or minimum wage protections.

Policymakers in several cities have tried to change that.

Seattle is a good example. In January 2024, the city implemented a law requiring delivery apps to pay drivers a minimum rate for each task: a combination of per-minute and per-mile minimum compensation that set a floor of US$5 per delivery.

The goal was straightforward: ensure that the people bringing you your lunch earn a decent living.

We are labor economists who have extensively studied the emergence of the gig economy and previous policy efforts designed to provide economic security to workers in unstable employment situations. We wanted to know how new gig economy regulations like the one in Seattle were playing out in practice.

When we studied what happened to delivery drivers’ earnings after Seattle’s payment rule took effect, we found that despite base pay per delivery roughly doubling, their total monthly earnings barely changed. That’s because competition among drivers for delivery tasks intensified while customers made fewer orders and tipped less on each order in the aftermath. Those effects combined washed out almost all of the intended gains.

No change in monthly earnings

To understand the policy’s effects, we used detailed data from Gridwise, an app that gig workers use to track their earnings across multiple delivery and ride-sharing platforms. This gave us an unusually complete view of how much the drivers were earning across all of the apps and platforms they were using.

We compared what happened to the earnings of drivers who were primarily working in Seattle before the law took effect with the earning of drivers working in other parts of Washington state, where nothing had changed. By tracking both groups over the months before and after the policy, we isolated the policy’s impact from broader trends affecting all drivers.

Base pay per delivery in Seattle jumped from about $5 to over $12, as intended. But base pay is only part of the picture. Tips typically make up most of a platform delivery driver’s income, since customers generally tip 10% to 20% of the cost of their meals.

After the law took effect, tips fell sharply. Delivery apps passed higher costs on to consumers through new fees. DoorDash added a roughly $5 “regulatory response fee” to Seattle orders, and customers responded by tipping less.

Some platforms went further: Uber Eats removed the option for Seattle customers to tip at checkout. The drop in tips offset more than one-third of the base pay increase.

The other major change was that drivers started completing fewer deliveries.

Beginning in the second month after the policy took effect, Seattle drivers who had been consistently active on the apps prior to the change completed roughly 20% to 30% fewer monthly deliveries than they would have without the policy.

Importantly, these drivers didn’t leave the apps. They were still logging on and spending about the same amount of time working. They just weren’t getting as many delivery offers.

What were drivers doing with all that extra time on the app? Our data shows they were spending more of it waiting.

The share of on-app time spent actually performing paid deliveries fell substantially. Wait times between tasks increased by about five minutes, nearly doubling from pre-policy levels. And drivers went farther between deliveries – suggesting they were actively cruising toward restaurant-dense areas to find their next task, burning more gas without being paid for those extra miles they were logging.

Put those pieces together – higher pay per delivery, but fewer deliveries and lower tips – and they almost exactly cancel out. After a brief bump in the first month, monthly earnings returned to pre-policy levels.

Why gig markets are different

To understand why this happened, it helps to think about how gig delivery markets differ from traditional employment.

In a conventional job, raising the minimum wage creates a clear divide: Workers who keep their jobs earn more, while others may struggle to find work if their employers cut jobs.

But in gig delivery, there’s no such divide. There’s no hiring or firing involved; anyone can download the app and start looking for work. Delivery tasks are distributed among everyone who is online, and there’s no sharp boundary between having a job and not having one.

When what drivers get paid per delivery rises, gig work becomes more attractive, drawing new drivers into the market. Meanwhile, higher costs to pay drivers are passed along to consumers through increased delivery prices, which can lead to fewer orders and lower tips. More drivers chasing fewer deliveries means longer waits for tasks.

This process continues until the higher pay per task is fully offset by the longer gaps between paid work.

Our data confirms this pattern.

While deliveries by existing drivers fell sharply in Seattle, new entrants arrived. Within three months, newcomers were doing most of Seattle’s deliveries.

What this means going forward

To be sure, gig workers’ low pay is a real problem. The impulse behind Seattle’s law reflects legitimate concerns.

But our findings do suggest that efforts to directly regulate what gig workers earn per task they complete won’t easily fix that problem.

As long as anyone can join the platform and start competing for deliveries, the guarantee of higher pay per task will attract more drivers until the benefit is competed away through longer wait times.

Other cities and states are choosing this route

Actually raising earnings might require limiting the number of active drivers – something like the taxi medallion systems some cities once used to ensure high driver pay.

But entry barriers undermine the flexibility that draws many people to gig work in the first place. And platform behavior matters too: If apps eventually restore normal tipping features rather than strategically discouraging tips, which New York City and some other jurisdictions are now requiring, the picture for drivers could improve somewhat.

Still, there may not be a solution that preserves all the benefits of the current system while also guaranteeing higher pay.

Nevertheless, several cities across the country are considering similar regulations.

New York City implemented its own minimum pay rate for delivery workers in late 2023. City councils and state lawmakers in Chicago, Colorado, Minnesota and elsewhere have proposed similar protections.

Seattle’s experience suggests all cities should proceed with caution and be aware of the limits of what per-task pay regulations can achieve when the door is always open to new workers.

To purchase access to the data used in this study Brian K. Kovak received funding from the Block Center for Technology and Society at Carnegie Mellon University.

– ref. Seattle tried to guarantee higher pay for delivery drivers – here’s why it didn’t work as intended – https://theconversation.com/seattle-tried-to-guarantee-higher-pay-for-delivery-drivers-heres-why-it-didnt-work-as-intended-276576

Source: The Conversation – USA (3) – By Beth DuFault, Assistant Professor of Marketing, University of Portland

You check your credit score before applying for an apartment. Your fitness watch tells you whether you slept well enough. A workplace dashboard measures your productivity. Parents can buy devices that track their baby’s breathing and heart rate while they sleep.

Increasingly, numbers tell us how we are doing.

These systems promise something appealing: clear feedback about whether we are behaving well. They appear objective, neutral and data-driven. But they also signal a deeper cultural shift, as algorithms define what counts as virtuous behavior.

In other words, we are living in a world where metrics are being translated into moral judgments. As a researcher who has long studied how markets and technologies shape moral responsibility, I’ve seen how these metrics quietly reshape how people understand themselves and how other people judge them.

Defining the good life

For generations, religious congregations structured everyday life for many people, offering templates for identity and for what a “worthy” life should look like.

As societies grow more diverse, however, and as fewer people affiliate with formal religious groups, faiths’ moral influence on society is waning. With their authority no longer taken for granted, some religious groups market themselves almost like brands: lifestyle choices that one can choose to follow or ignore.

People start to assemble their own sense of right or wrong from a patchwork of sources – and increasingly, that involves for-profit scores, rankings and dashboards.

Credit scoring offers a clear example of how this works. A credit score seems like an objective measure of financial worthiness.

But the actions required to optimize a score define what worthy financial behavior looks like in U.S. society today. It’s not just about paying bills on time. Achieving an optimum credit score most often involves having at least one credit card; keeping a low debt-to-credit ratio, which might involve requesting credit limit increases instead of paying down debt; not canceling any credit cards so that average account length is maximized; and having the “right” credit mix, which often includes a consumer loan. Today, a consumer with no credit cards – something that at one time might have seemed financially virtuous – doesn’t develop the kind of “file” that is readily rewarded with a high score, and they might not be able to obtain credit to buy a house or a car.

In our work on consumer credit scoring, consumer culture researcher John Schouten and I found that people often incorporate their credit scores into their sense of identity and narrative about their life, interpreting scores as reflections of their character and morality. A high score feels like a sign of virtue. A low score can trigger feelings of shame or failure and a determination to be better.

One consumer described discovering her credit score for the first time as finding out what kind of person she actually was. Another, working to rebuild his score after a medical debt caused a cascade of defaults, related that he checked it every morning, to see if he was someone people could trust again.

Moral mirrors

Credit scoring is only one example. Health apps convert exercise, sleep and heart rate into performance indicators. Workplace platforms turn everyday tasks into dashboards, rankings and streaks. Reputation systems rate drivers, sellers and freelancers, often with a single number that stands in for trustworthiness.

Even parenting, one of the most emotional human roles, is touched by this logic. Wearable infant monitors translate babies’ breathing, oxygen levels and sleep patterns into charts, alerts and “insights.” These technologies are marketed as tools for reassurance, but in a 2026 paper, my co-authors and I found that these tools also nudge expectations.

Parents describe feeling that if a device exists that can watch a baby’s breathing all night, then a truly responsible caregiver must use it. “All the parents in our social group have one breathing monitor or another,” one dad said. “My boss has one. If I could prevent something horrible by spending a little money and watching the monitors, and I didn’t, what kind of parent would I be?”

The emotional weight of that shift is striking. One mother said that she felt guilty on the nights she forgot to charge the device – not because anything had gone wrong, but because she had failed to be watchful in the way the market now defines good parenting. Another said simply, “If something happened and I didn’t have it on, I don’t know how I could live with myself.” The monitor had become less a tool than a test.

Measurement can be genuinely useful. When scores appear precise and impersonal, they can feel more solid than the messy, subjective judgments we make in everyday life. But as historian Jerry Muller lays out in “The Tyranny of Metrics,” scoring systems subtly embed assumptions about what responsible behavior looks like, then reflect those assumptions back to us as if they were simple facts. A high credit score begins to look like proof of moral worthiness. A steady stream of productive hours on a work dashboard looks like evidence of commitment.

As these metrics spread, they start to stitch together a new, data-driven sense of what it means to be a good person. This shows up in ordinary decisions: choosing a loan because it will help your score; taking your phone on a run so it “counts” toward your fitness goals; waking in the night to check a baby only because the app suggests you should. The line between caring for others and optimizing for a number becomes easy to blur.

Into the void

For centuries, religious traditions, philosophers and moral communities have wrestled with what it means to live a good and virtuous life. Algorithmic scoring systems do not claim to answer those questions, but as traditional forms of moral authority weaken among many Americans, I would argue that algorithmic systems are moving into the void.

They do not claim to answer questions about the soul, but they do offer something that can feel almost as reassuring: clear indicators of whether you’re on the right track. A high score, a green check mark, a completed streak – these are small, everyday reassurances that we are, in some sense, measuring up.

The deeper question is how comfortable society is letting these systems become our go-to mirrors for moral self-assessment. Instinctively looking to a number to tell whether someone is doing well as a borrower, worker, patient or parent risks forgetting that numbers can only capture a thin slice of what it means to be a good human being.

Many of these scoring systems are built by for-profit companies with a specific interest in the outcome. They are not designed simply to measure behavior; they are designed to shape it, nudging consumers to continuously improve their scores in ways that make them more valuable, more legible and more profitable to the companies doing the measuring. The goal is not necessarily for you to flourish; it’s for your behavior to benefit corporations.

The next time you check your rating or a ranking and feel a small surge of pride or unease, it may be worth pausing to ask: Whose idea of “good” am I seeing reflected there, and is it really the one I want to live by?

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

– ref. Moral metrics: Are corporate algorithms becoming our new moral authorities? – https://theconversation.com/moral-metrics-are-corporate-algorithms-becoming-our-new-moral-authorities-273178