I’ve heard good things about Altberg. Apparently they make boots for the police. I heard of them from a friend who was on the police, and loved her boots so much she sprung for their bespoke service.

Yeah, and I would add that even if OP does get a small token of appreciation for their neighbour, but it would be good to emphasise that OP doesn’t feel like the gift has paid their neighbour back for their help, but just a little extra, and that the neighbour should let OP know in future if there’s any way for them to help out.

I say this as someone who often explicitly prefers to not be paid back for kindnesses done unto others. I’d prefer someone to pay me back in kindness in the future. After all, if I’m in a position to help someone out, then it’s likely that I’m not in particular need of receiving extra kindness at that point in time. However, if I remain friends with someone for long enough, inevitably our positions will end up being reversed at some point. It’s nice to feel like I’m a part of a dynamic little equilibrium.

And if there never comes an opportunity to pay me back, then paying it forward works for me too. None of us can escape living under late stage capitalism, but small acts of community building feel like a hint of an escape

This is a great explanation

I don’t have any suggestions, I just want you to know that you’re delightful

I’m using this:

“that crime statistics need to be carefully considered because of a large risk of bias in police responses to things let alone the justice system itself.”

to argue that the data behind these statistics are so riddled with bias that I am extremely dubious about them, to the extent that I think it’d be epistemologically safer to largely disregard the stats.

I mean, I’m a scientist, and so my whole thing is about grappling with the fact that statistics are just a proxy for the thing we actually care about. But the thing that makes statistics useful in science is being able to estimate how uncertain we are in our data — if we don’t have sufficient understanding of the data and how much it’s affected by bias, then it’s pointless to rely on it in our analyses. Less than pointless, actually, because it’ll lead us to a false sense of confidence where we think we somewhat understand some phenomena, but in reality we’re digging in the completely wrong areas.

By mentioning racial bias, I was making the wider point of how the statistics rely on data that is inherently biased due to how it was collected; Inequality in policing and the judicial system leads to different outcomes.

A concrete example from the UK is that in the year ending March 2024, under the “stop and search” procedures, “there were 59,549 searches of women, […] and 447,952 searches of men […]” (Elided parts of the quote are because the article compares stats to the year prior, which isn’t relevant to our discussion)

That’s a ratio of men and women being searched of around 15:2 . I’m going to treat that as if it were 7:1, because I want to set up a hypothetical. Now obviously this doesn’t include any of the downstream stuff like rates of actually getting arrested, or later found guilty, because that would be far too complex to consider here. Let’s treat stop and search rates as a proxy for crime rates, and consider two different scenarios that could explain these data.

In scenario 1, we would assume that for each gender, the number of people stopped and searched is proportional to the number of people who commit crimes, I.e. that:

the gendered ratio of stop and search (7:1) ≈ the gendered ratio of crimes committed (7:1)

Now for scenarios 2, let’s assume that this isn’t the case, and that actual ratio of crimes committed is 𝒳 :1, where 𝒳 is unknown; although my belief is that 𝒳 lies somewhere between 1 and 7 (i.e. that women commit more crimes than is recorded in the stats, but likely not significantly more than men do), 𝒳 could even be larger than 7.

There’s a lot of possible reasons why we might find that 𝒳 ≠ 7. Police may actually use stop and search as a tactic to harass women (depressingly common based on what we’ve seen of police abusing their power against women), leading to women being over counted in the stats compared to their actual crime rates; or maybe police are less likely to stop and search women because they’ve found that to find contraband like drugs, a more invasive search would be necessary (I, and many women I have known have occasionally hid small, secret items in their bras, and I imagine many criminals would have had the same idea); or maybe police officers are worried about being accused of abusing their power to harass women, so their personal sense of professional risk leads them to be less likely to stop women. I’m not trying to make the case for any of these in particular, merely assert that there are many plausible reasons why the ratio of stop and searches might be different to the ratio of crimes committed.

In both scenario 1 and 2, our data shows us the same thing: that men commit more crimes than women at a roughly 7:1 ratio. However, in scenario 2, this conclusion is an incorrect one, due to bias in how the data was collected. The crux of my point is that we don’t know whether reality is closer to scenario 1 or 2, and we don’t have a way of knowing because we have no way of counting true rates of crime; anything that tries to study crime is inevitably going to have a heckton of false negatives — that is, criminals who get away with it. And every innocent person who has been imprisoned is a false positive. False positives and false negatives are a problem in any statistical study, but I am arguing that this is especially significant in this case due to well documented inequalities in policing, affecting multiple axes of oppression. That’s why I brought up racial bias — to highlight the many flaws of policing as a method of data collection.

Often when we run into the problems of false positives and false negatives in statistics, we are able to estimate how accurate our proxy measurements are by comparing them to a reference gold standard. During COVID, for instance, when Lateral Flow Tests (LFTs) were being tested, we were able to test them against PCR tests, which were known to be extremely accurate. We have no such reference standard when it comes to crime stats — all we have is the proxy. What I am advocating for is that we keep this in mind, and take any crime statistics with a hefty dose of salt

To somewhat play Devil’s Advocate, I would highlight that the stats don’t show who commits more crime, but who gets caught more. If no-one arrests you, (or if a court finds you not guilty), you won’t be in the stats.

In my country, for instance, police can stop and search you if they have “reasonable suspicion” that you’re carrying something illegal (stolen good, drugs, weapons etc.). If police are operating under the assumption that men commit more crime than women, they’re far more likely to be suspicious of a man committing the same crime as a woman.

I haven’t read anything that’s about gender bias specifically at this level of policing, but I do know there’s a lot of research (especially in the US) on how racial bias causes black neighbourhoods to be more heavily policed than neighbourhoods with comparable crime levels, leading to a self-reinforcing cycle where heavier policing leads to increased belief that black people commit more crime, which leads to heavier policing^[1][2]

I do know that after an arrest has been made, women tend to fare better than men; they are less likely to be sentenced, and when they are, they tend to receive less severe sentences than men, even for equivalent crimes^[2][4] . This is speculative, but I imagine this has a cascading effect — if there is a crime where the punishment could range from community service to a prison sentence, then the person who gets community service is statistically less likely to reoffend than the person who goes to prison^[3]

All that in mind, I’m pretty confident that the gender ratio in crime statistics gives a skewed impression of who actually commits more crime, and that women are effectively undercounted if we’re talking about who commits more crime — though I can’t guess on to what degree this is the case. However, it’s entirely possible that women commit crimes at a similar rate to men, or even at a higher rate. We can’t really know.

And to finish off this comment with a slightly more shitposty answer that still links into my broader point, it’s possible that women commit as much crime as men, but the statistics are skewed towards men because women are more effective criminals.

I include this last possibility because I am uncomfortable with how you framed things in your question, with phrases like “crime being disproportionately committed by men is a universal constant”. Statistics are never Truth, and are, at best, only ever an approximation. Stats can give us a sense of clarity in an overwhelming world by reducing down complexity into much more easily parsed, quantitative data, often presented in an easy to visualise manner. It feels objective. However, statistics only serve to mask the underlying bias in what data we choose to collect, who collects it, and how — which means that treating statistics as objective can be dangerous due to making us less aware of biases and inequality, and thus even less objective.

I like the way that the feminist philosopher Donna Haraway puts it; she describes data visualisations as “the god trick of seeing everything from nowhere”^[4][5]. I’mma quote a long passage from an excellent book here, because I don’t think I can explain it any better than this:

“The view from nowhere—from a distance, from up above, like a god—may be data visualization’s most signature feature. It’s also the most ethically complicated to navigate for the ways in which it masks the people, the methods, the questions, and the messiness that lies behind clean lines and geometric shapes. Haraway calls it a trick because it makes the viewer believe that they can see everything, all at once, from an imaginary and impossible standpoint. But it’s also a trick because what appears to be everything, and what appears to be neutral, is always what she terms a partial perspective. And in most cases of seemingly “neutral” visualizations, this perspective is the one of the dominant, default group.” ^[5]

To bring things back to our question, I strongly believe that we don’t know if men commit more crime than women. I think it’s plausible that it could be true, but due to inherent bias in how the data behind these statistics are gathered (i.e. documented inequalities in policing and sentencing), we simply don’t know. Statistics always carries this problem of bias being hidden in the data, but it’s especially tricky when dealing with complex socioeconomic matters such as crime. To me, this is a standout example of an area where we need to be especially cautious that we don’t mistake the stats for truth.

[1]: Open Access Academic Paper:
“Smartphone Data Reveal Neighborhood-Level Racial Disparities in Police Presence”, (2023), Chen et al.
https://doi.org/10.1162/rest_a_01370

[2]: More accessible summary of [1]
https://anderson-review.ucla.edu/smartphone-records-reveal-racial-disparities-in-neighborhood-policing/

[3]: Paywalled Academic Paper:
“Gender Disparities in Sentencing”, (2020), Arnaud Philippe
https://doi.org/10.1111/ecca.12333
Unpaywalled SciDB mirror via Anna’s Archive

[4]: More accessible summary of [3], by Michelle Kilfoyle and Arnaud Philippe
https://ceps.blogs.bristol.ac.uk/2021/11/17/gender-stereotypes-see-female-criminals-fare-better-in-court/

[5]: Old Academic Paper:
“Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective,” Feminist Studies 14, no. 3 (1988): 575–599
Quote and reference retrieved via [6]

[6]: Open Access Book “Data Feminism”, (2020), Catherine D’Ignazio and Lauren Klein
Fairly academic, but also quite accessible to anyone interested in how socioeconomic inequality shapes how we use data, and how data feeds inequality. I highly recommend this book, it is excellent
Quoted section found here

And on the off chance one of you delightful nerds would like to read more, here is a link to the main book page, for your convenience:
https://data-feminism.mitpress.mit.edu/

^(It’s funny that now I’m no longer in academia, I seem to have fun writing cited essays. Though to be fair, I studied biochemistry, so this is outside of my main wheelhouse — which is probably why I’m so diligent with citing my claims)

Huzzah for wholesome anarchy!

Thanks for indulging my curiosity

The command isn’t working for me and the URL in the command doesn’t seem to work anymore. Could you check for me please? I’d be surprised if I was on it, given that I generally try to be wholesome and civil in my online conduct, but I’m curious.

I like crochet or knitting. When I’m working on something, there’s usually a period where I do have to focus on the task, but once I get going, I love how I can just do it ambiently, as I’m either doing something else, or nothing at all — I used to take crochet to my university lectures, and it actually helped me to focus on the lecture.

Similarly, when I made a chainmaille hauberk, I liked how easily I could just zone out once I had a bag of rings and I just needed to interlock them, using pliers. When I was piecing all of it together, I needed to focus, but I spent tens of hours just mindlessly linking rings together