I bet this doesn’t match people’s intuitions – particularly given the volume of media coverage. Data here.
Religion in England and Wales (2011 and 2021 census data)
Category: Ceci n’est pas une catégorie
Deaths in immigration detention
The latest statistics are available up to end of 2024 here (tab Det_05b has a breakdown by centre):
| Year | Number | Sex | Age | Nationality | Cause of death | Place of incident | Place of death |
|---|---|---|---|---|---|---|---|
| 2017 | 1 | Male | 18 to 29 | Poland | Self-inflicted | Morton Hall IRC | In detention |
| 2017 | 1 | Male | 30 to 49 | Poland | Other | The Verne IRC | In the community |
| 2017 | 1 | Male | 30 to 49 | Slovenia | Self-inflicted | The Verne IRC | In detention |
| 2017 | 1 | Male | 18 to 29 | Poland | Self-inflicted | Harmondsworth IRC | In the community |
| 2017 | 1 | Male | 50 to 69 | China | Natural causes | Dungavel IRC | In detention |
| 2017 | 1 | Male | 30 to 49 | Jamaica | Natural causes | Morton Hall IRC | In the community |
| 2017 | 1 | Male | 18 to 29 | Iraq | Self-inflicted | Morton Hall IRC | In detention |
| 2018 | 1 | Male | 50 to 69 | Algeria | Natural causes | Harmondsworth IRC | In detention |
| 2019 | 1 | Male | 30 to 49 | Nigeria | Other | Harmondsworth IRC | In detention |
| 2020 | 0 | ||||||
| 2021 | 1 | Male | 30 to 49 | Czechia | Self-inflicted | Morton Hall IRC | In detention |
| 2021 | 1 | Male | 30 to 49 | Romania | Natural causes | Dungavel IRC | In the community |
| 2022 | 1 | Male | 30 to 49 | Iraqi | Natural causes | Manston STHF | In the community |
| 2023 | 1 | Male | 30 to 49 | Columbia | Self-inflicted | Colnbrook IRC | In detention |
| 2023 | 1 | Male | 30 to 49 | Albania | Self-inflicted | Brook House IRC | In the community |
| 2024 | 1 | Male | 18 to 29 | France | Natural causes | Brook House IRC | In detention |
The stats for 2025 are due to be published 27 August 2026. Release dates are in the Research and statistics calendar.
The Westminster Model and why it’s useful even though it’s wrong
This paper by Paul Cairney (2025) is fun.
The Westminster Model (WM) of policymaking:
“… political parties present manifestos to compete for public votes in general elections, the winner forms a government, and government ministers oversee the delivery of their manifesto by civil servants and public bodies.” (p. 297)
What’s wrong with the British Political Tradition (BPT), the UK flavour of the model:
“… politicians do not live up to the BPT; party manifestos combine some specific and many vague promises; parties don’t always gain a majority; not all MPs are easy to keep in line with government policy; ministers could not understand all of the choices made in their name; EU entry and exit was a bruising experience; and, the UK’s ability to reform devolved or subnational government is constrained in practice. In other words, the first aim of a ‘governance thesis’ is to use the WM as an ideal-type to compare with more accurate stories of the real world.” (p. 298)
Cairney then argues that “these differences between aspiration and reality provide the launching point for a more realistic story” – and goes on to have a go, e.g., noting the impact of the finite time and capacity of ministers, expanding on the range of individuals and informal and formal groups that influence policymaking
The approach reminds me of how classical logic and probability are used in the psychology of reasoning: they offer frameworks for understanding how people ought to reason, and then researchers adjust that framework to match how non‑mathematicians actually do reason, constrained by finite working memory, drawing on broader interpretations of terms like “if” than are used in logic, etc.
References
Cairney, P. (2025). Governance and the Westminster model: What exactly is the dominant story of UK policymaking? British Politics, 20(3), 295–315.
Theory-based evaluation as tautology
“Realist” evaluation and the more general “theory‑based” evaluation umbrella often present themselves as distinct forms of evaluation; however, their core assumptions that programmes work (or not) through underlying causal mechanisms that cannot be directly observed are restatements of what sciences already presuppose. It’s rare indeed for an evaluation not to begin with a theory of change; the issue is not whether that’s necessary but how good theories are. We’ve all seen boxology lists in the logic model style.
The full range of evaluation methods can test mechanisms rather than only whether an outcome has been shifted by a programme and current practice involves qual and quant evidence, e.g., the latter using mediation tests. The logic of “what works best for whom in what context” predates “realist” approaches by a few decades and is used across all methods. Similarly, there’s nothing uniquely “realist” about context-mechanism-outcome triads: it’s a useful way to move from boxology to theory prose.
Advocating “realist” or “theory‑based” evaluation is essentially advocating “scientific evaluation” – a kind of tautology. It may be helpful for some readers to learn about separating ontology from epistemology (the map is not the territory, to paraphrase Korzybski), but I’d suggest that’s best left to the first chapter of an introduction to evaluation rather than something that needs to be restated in every report. There are examples of fields that stick on the “science” term: neuroscience and cognitive science are two examples. But what would unscientific policy evaluation look like? What does it get called? (That’s a genuine question – I have a blog post brewing.)
Theorising what evidence would test a theory
Policy evaluation involves speculating about what might be going on – a creative process – based on what we currently (think we) know, and then working out what evidence we would expect to see if we were correct or if plausible alternative theories were true. That logic is explicit in process tracing, but it is far more general than that. The oft‑cited Van Evera (1997) rules, such as the “smoking gun”, follow directly from Bayes’ rule. We cannot directly observe a programme’s mechanisms of change; many different theories will be compatible with both qualitative and quantitative evidence. That problem has a name, underdetermination, and it is a challenge across the sciences.
An interesting example of the gap between theory and evidence is how physicists detect gravitational waves produced by black holes merging. The Laser Interferometer Gravitational-wave Observatory (LIGO) does not observe the waves themselves; it measures how those waves distort spacetime by a miniscule amount, far smaller than a proton.
My favourite analogy comes from trying to crack encrypted texts. We can systematically analyse letter and word frequencies in ciphertexts to spot patterns, but that only takes us so far (try the exercises in Rubinstein‑Salzedo, 2018). It helps to guess what people might be trying to say to each other based on something beyond the ciphertext; for example, that they open with “How are you?” or that they are likely to be discussing a particular event. These knowledge‑grounded guesses, drawing on a wealth of experience and prior evidence, help reduce the search space of possible encryptions. Something similar is going on in evaluation when we iterate between programme theories, tests of those theories, and exploratory research to generate ideas for new theories.
References
Rubinstein‑Salzedo, S. (2018). Cryptography. Springer.
Van Evera, S. (1997). Guide to methods for students of political science. Cornell University Press.
Interventions to reduce social media use
Goldfield et al.’s (2026) RCT looks interesting – the intervention was reducing social media use to max an hour a day. They found a 0.11 reduction in loneliness versus control on the UCLA Loneliness Scale. SD at endline was 0.65, so standardised mean difference (SMD) = 0.17.
A systematic review (Burnell, 2025) found an SMD of 0.12 (95% CI -0.02 to 0.26) for loneliness, 0.16 (CI = 0.04 to 0.28) when they screened out one study judged to have issues, leaving 13. (Haven’t waded into the details of each intervention or what those issues are.)
References
Burnell, K., Meter, D. J., Andrade, F. C., Slocum, A. N., & George, M. J. (2025). The effects of social media restriction: Meta-analytic evidence from randomized controlled trials. SSM – Mental Health, 7, 100459.
Goldfield, G. S., Lopes, M. V. V., Mahboob, W., Perry, S., & Davis, C. (2026). Reducing social media use decreases loneliness regardless of gender or level of social comparisons in youth with anxiety and depression: A randomized controlled trial. Journal of Affective Disorders, in press.
“Yes, it did”
As innumerable hacks read their tea leaves to forecast what happens next, and debate who is responsible for whose downfall, this shows what an effective Opposition moment looks like – and why the Opposition sometimes, very occasionally, strengthens democracy (House of Commons, Wednesday 4 February 2026):
Mrs Badenoch: On 10 September, when we knew this, I asked the Prime Minister about it at the Dispatch Box, and he gave Mandelson his full confidence—not once but twice. He only sacked him after pressure from us. I am asking the Prime Minister something very specific, not about the generalities of the full extent. Can the Prime Minister tell us: did the official security vetting that he received mention Mandelson’s ongoing relationship with the paedophile Jeffrey Epstein?
Prime Minister: Yes, it did. As a result, various questions were put to him. […]
Mrs Badenoch: What the Prime Minister has just said is shocking. How can he stand up there saying that he knew, but that he just asked Peter Mandelson if the security vetting was true or false? This was a man who had been sacked from Cabinet twice already for unethical behaviour. That is absolutely shocking.
How likely or unlikely do you think it is that Keir Starmer will still be prime minister at the end of 2026? (YouGov, 5 January 2026, 4,667 GB adults, verys and fairlys summed):
| All | Con | Lab | Lib Dem | Reform | |
|---|---|---|---|---|---|
| Likely | 29 | 19 | 45 | 39 | 15 |
| Unlikely | 55 | 70 | 39 | 44 | 76 |
| Likely – unlikely | -26 | -51 | 6 | -5 | -61 |
| Don’t know | 17 | 10 | 15 | 16 | 8 |
The cost of dichotomisation
‘We provide a method to convert a sample size calculation for the comparison of two proportions into one for the comparison of the means of the underlying continuous outcomes. This demonstrates how much the sample size may be reduced if the outcome were not dichotomized. We also provide a method to calculate the loss of information after a dichotomization. We apply this method to all the trials from the CDSR with a binary outcome, and estimate that on average, only about 60% of the information is retained after dichotomization. We provide R code and a shiny app at: https://vanzwet.shinyapps.io/info_loss/ to do these calculations. We hope that quantifying the loss of information will discourage researchers from dichotomizing continuous outcomes. Instead, we recommend they “model continuously but interpret dichotomously”.’
Van Zwet, E. W., Harrell, F. E., & Senn, S. J. (2026). An Empirical Assessment of the Cost of Dichotomization of the Outcome of Clinical Trials. Statistics in Medicine, 45(3–5), e70402.
Explaining evaluation to the public
If you have ever introduced yourself to someone as an “evaluator”, you will quickly discover that very few people outside the field have the first clue what that involves.
The term is incredibly broad. Am I evaluating your dress sense or how you dance? Am I running psychometric assessments to evaluate staff performance? We are all evaluators, regardless of how often we share the judgements.
One of the functions of the UK Evaluation Society is to explain to the public what we do. So, how can we do this?
I think there are two main communication challenges for evaluation. The first challenge is finding a concise term that signals what evaluation involves to people who do not work in the field. It seems likely the easiest way to do this is simply to add a word or two to “evaluation”, so the problem is: which words? The second challenge is to find ways to explain what evaluation involves in practice, by using clear, straightforward language that can also ideally be applied more consistently in evaluation reports.
Words for evaluation – the case for social policy evaluation
Given how baffled people tend to look when I call myself an evaluator, I’ve previously said I’m a social researcher and work in social policy, and then just explained what that means through examples. Since October, I’ve tried “policy evaluator” or “social policy evaluator” and they seem to fare surprisingly well. The most obvious alternative to these might have been “programme evaluator”, but my problem with this is that I think programmes are more obviously associated with software or what’s on TV.
One possible objection to the term policy evaluation is that policies are rather broad, e.g., the Green Book (p. 6) says a policy is “a statement of intent that is implemented through a procedure or a protocol and a deliberate system of principles to guide decisions and achieve rational outcomes.” Evaluations often focus more specifically on programmes and service delivery. However, programmes put policies into practice, and evaluation findings should inform those policies. If a programme is shown to work, that has clear implications for policy. Plus, policies aren’t just found in government: schools, charities, and other organisations have them too. I think it’s important to show we engage in this broader context.
Explaining what we do
Policy evaluation is a broad field, covering many sectors, services and types of activity. It can look at whether public services, the work of charities, or even individual practitioners are making a meaningful difference through what they’re doing. It might tackle local programmes or national reforms. Evaluation also uses the full range of methods, mixing qualitative and quantitative evidence, in large surveys and focused case studies. Examples that illustrate this variety of topics and methods are essential, as Sarah Mason highlighted in one of the few studies exploring how evaluators explain their work to the public.
Some of the terminology we use within evaluation contexts is unnecessarily opaque and misleading, which is unhelpful for understanding the role. A good example – a word I’ve used more often than I should – is “treatment”. We’re not administering drugs – we’re evaluating programmes like mentoring, approaches to teaching, or emotional support. The term “treatment” carries medical baggage, that doesn’t fit with social programmes. My preference would be to label study conditions after what’s actually being tested. For instance, if we were evaluating ACME Therapy, the conditions would be something like ACME Therapy and usual practice. Using medical language in the context of trials in schools or criminal justice can sound especially creepy – it reminds me of A Clockwork Orange.
Evaluating how we explain ourselves
As policy evaluators we should evaluate how well we communicate with the public. For example, participants could be provided with information about what evaluation is and how it is used, enabling them to take an informed, participatory role in co‑designing how evaluation itself is explained. We could then use surveys, following Sarah Mason’s example, to test the effectiveness of different ways of explaining evaluation at a larger scale.
Evaluation is a broad transdiscipline, working across a wide range of contexts, so it is unlikely that we will find a single way of explaining ourselves that works everywhere. However, a collection of different terms and explanations that work well in at least some contexts would be valuable in reconnecting our work with the people we hope to serve. This would also help to attract new policy evaluators to what is one of the most important and exciting social sciences.
Originally posted on the UKES blog (February 4, 2026).
Typos
Just spotted a typo in something I’ve written and it reminded me that aoccdrnig to a rscheearch at Cmabrigde Uinervtisy, it deosn’t mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm.
It was a meme, Cambridge never did that research. But I’ve just discovered that someone else did: Rawlinson, G. E. (1976) The significance of letter position in word recognition. PhD Thesis, University of Nottingham.