I miss my Nexus 6. It was my first Android phone, and got me into tinkering with android. I remember that the device codename, relevant for finding compatible ROMs, was “shamu”. I miss that big old whale

A lot of what I miss is the software side of things. Google Assistant used to be way more capable than it is now. And now they’re replacing that entirely with Gemini.

Yeah, it’s a skill like any other. For a lot of men I have known, that process was only able to start taking place once they had people in their lives who were safe to do this kind of exploration around. It always feels clunky at first, but with enough practice, it can become a habit.

And it does get easier too — especially because as you cultivate more healthy modes of being, it becomes easier to find people who help promote this kind of healthy vulnerability

Your last two paragraphs perfectly encapsulate a sentiment that I wanted to include in my comment, but couldn’t capture well enough.

Most importantly, love requires both partners to be emotionally vulnerable, right? So then under gender roles, men can’t love women, only objectify them? Wouldn’t this make society one giant trauma factory where men are traumatized into detached sociopathy and women are traumatized into servitude?

Yup, basically

So what the hell are conservatives doing in relationships? Are they all loveless relationships

I think that whilst there are some conservative men who “cheat” and allow themselves moments of escaping from their gender roles, there are a lot of miserable people. Like, have you ever noticed how conservative media is just constant ragebait, and makes people just get angry at how shit the world is, then it will make them feel scared and powerless, but then because they don’t have the emotional capacity to explore those emotions, they channel it more into rage. I think that you’re right on the money when you describe claiming power under the patriarchy as being a “Faustian bargain”.

Although you probably feel like you’re socially delayed because you’re not just now working through all this stuff, speaking as someone who’s 29, I have known plenty of people who didn’t reach this realisation until they were a fair bit older than you, so from my perspective, you’re probably ahead of your peers. Some people may seem like they don’t need to work on this stuff, but often it’s just that it’s less easy to detect until it really becomes an issue later in life. What you discuss is why I vehemently consider it to be an aspect of my duty as a feminist to work to understand how the patriarchy harms men, and restricts masculinity in general.

If you want to explore this more, a book that I would hugely recommend is “The Will to Change: Men, Masculinity and Love” by bell hooks. It’s an incredible book, and I think we’d live in a far healthier world if every man read this book. It’s old enough that you can find it for free on Archive.org

“One of her stories is during anal sex she defecated on a $10,000 fursuit”

Oof. However, I’d argue that if someone owns an expensive fursuit that is coming anywhere near to the act of anal sex, that’s just playing stupid games, which comes with the risk of winning a prize

This is the first one on this thread to really make me laugh — and your follow up comment makes it even better.

“AI data centers are just one of many things contributing to CO₂ emisions.”

A useful perspective is to reverse how we frame the causative logic here; the fossil fuel industry is contributing to the AI data centre madness. There’s a lot of evidence of fossil fuel executives and lobbyists pushing for AI because it helps to justify their existence in a world where renewable energy isn’t just cleaner, it’s cheaper.

What this means is that we have two problems to solve when it comes to CO2 emissions:

• Humanity’s apparent level of dependence on fossil fuels, when accounting for deliberately wasteful endeavours like mega data centres. and
• Humanity’s actual level of dependence on fossil fuels. This is higher than it should be and requires some changes to society to improve, but it’s not nearly as high as it might seem at a glance.

These are both problems we need to solve, but the method is different for esch

Okay, so there’s two main reasons why alcohol withdrawal is so brutal (much of what I’m about to say also applies for benzos too)

1.) Alcohol primarily increases the actions of a neurotransmitter called GABA, which is sometimes referred to as “the off switch of the brain” because of its role as the key inhibitory neurotransmitter. If you’ve ever heard people say that alcohol is a depressant, this is what this means (although people often use that to mean it will make you emotionally depressed, but that’s not what the phrase means — it means that it’s a central nervous system depressant. Certainly alcohol (and alcohol withdrawal) can affect mental health, but as a biochemist, that’s far out of my wheelhouse; I do know that the effect is not a simple one of “alcohol makes you sad”).

Chronic alcohol dependence means that the brain has to upregulate a bunch of systems, in order to compensate for the neuronal inhibition caused by extra GABA activity. It’s basically the same phenomenon as how if you regularly drink caffeine, it will stop being effective due to compensatory upregulation of the system involved — except when you stop drinking caffeinated drinks, you’re mainly feeling the impact of that one upregulated system. It feels awful, but it won’t kill you. However, GABA has such a far reaching impact across the entire central nervous system that when you stop drinking, your entire brain is running at an increased level of excitation — which is what can lead to things like anxiety, tremors and seizures.

2.) Another reason why alcohol withdrawal has such a big impact is because alcohol doesn’t just affect GABA, but many other neurotransmitters. It’s what my neuroscientist friend referred to as “a dirty drug” (n.b. there’s no value judgement in this statement, it’s just just referring to it being less specific than other drugs). Dopamine, serotonin, glutamate and norepinephrine (and others) are also affected by alcohol consumption, and as with GABA, chronic alcohol dependence causes these systems to adjust their level of activity to compensate for the alcohol. When a person stops drinking alcohol, it’s basically all of these compensatory mechanisms being suddenly revealed.

Below is a diagram that shows how chronic alcohol dependence affects neurotransmitters (an up arrow to the left of a neurotransmitter means that it is upregulated due to alcohol (i.e. systems involved become more active), and the symptoms on the left are the result of the changes to those systems, only revealed during alcohol withdrawal. I don’t expect you to be able to follow the specifics of this image, given that even in the textbook I got this from^[1], the main point of this image seems to be to go “There’s a heckton going on and everything is so interconnected that it’s super difficult to figure out exactly how alcohol withdrawal causes these symptoms”

[1]: citation for the image: Howard C. Becker, Patrick J. Mulholland, Chapter 9 - Neurochemical mechanisms of alcohol withdrawal, Editor(s): Edith V. Sullivan, Adolf Pfefferbaum, Handbook of Clinical Neurology, Elsevier, Volume 125, 2014, https://doi.org/10.1016/B978-0-444-62619-6.00009-4. (https://www.sciencedirect.com/science/article/pii/B9780444626196000094)

I’m a mediocre hobby programmer, and for me, the hardest part is coming up with the concrete specifics of what to implement.

I’m actually half decent now (for a hobbyist), but I remember that when I first moved from coding along with worked exercises in textbooks, to actually trying to solve real life problems, it got exponentially more difficult. The textbooks say “use this tool to achieve this goal”, and even if you’re bad at using that particular tool, you can put all your effort into doing it well. However when you’re having to make the bigger decisions for yourself, you have to choose what tool to use, and I found that super hard — especially when having to choose between an approach that I felt confident with that wasn’t quite the right tool for the job, and a new tool that was completely new to me (and if I am unfamiliar with a new approach, am I sure that it’s the right tool?).

At one point, when I was deep in a personal project, I had a period of a few weeks where I had no reliable access to internet, and so I did most of my coding using paper and pen. It forced me to think more deeply about the implementation I was using, rather than just diving straight into the code

Yeah, I feel like the author of this blog post read the referenced piece in a quite adversarial manner. Even if a programmer does no talking to stakeholders at all, the planning and design part of the process is still huge, and largely invisible if all you’re looking at is the end result. That invisible skill and labour is the difference between a mediocre personal project of mine, and the output of a seasoned professional.

I’m not AI, just struggling to articulate myself in matters like this. Though I can see why you thought it was AI — reading it back, I can see that there are a few phrases that are common AI-isms. I’m pretty opposed to AI use at the best of times, but I agree with you that it would be fucked up to use it for this.

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

Even just the video (before the reaction to criticism) left a super bad taste in my mouth. I remember an aggrieved knitted described their own feelings around it as being that it was easy to enjoy the edutainment material he produces when it was in a topic they were confident he knew more than them on. However, when they saw how shoddy the knitting video was, that made them feel uneasy about even the non-knitting related content, because what if that was as plagued with errors and bias as the knitting video, except they didn’t notice it.

What I was especially salty about with the video is that there was a non-problematic way to say “look at the cool research that involves throwing modern science at ancient textiles crafts” — highlighting research by people like Elisabetta Matsumoto, a physicist and a knitter. I was so hyped when someone reviewing the video mentioned Matsumoto’s research, as I was aware of her research already from being a ridiculous nerd.

I’m sorry that things got bad enough to require inpatient care, but I’m glad you were able to get some help — I hope it did actually help. I know from firsthand experience that one never fully recovers from PTSD, in the sense that you can’t go back to how things were before. However, I also know that it’s possible to continue living life with the psychological equivalent of a massive scar, even if it is still red and angry and often painful — but not really possible to when instead of a scar, you have an open, gaping wound.

I hope you were able to make some progress towards healing, even if it’ll take much longer to fully heal.

I also appreciate your attempt to leverage your own experience to promote compassion for Green. I’ve not read the rest of the comments here yet, but I can imagine there are a lot of people with uncharitable stances towards the situation. I am pretty steadfastly anti-AI, but I think it’s important that we consider the person at the centre of any problematic AI use

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