.fandom | .gallery (artworks) | .probability (misc) | .animals |.reality - (politics) | .starlight (space/quantum/patterns) | .library (guides & writing) | .bio (logy) | .adjacent (fandoms I don't go to) | .timeline (history) | .osgr.osgr (loops/recursion) | .inanimate - (cool inanimate things) | .filename (on making art) | .food | .graph (polls) | .casette (audio/video) | os.gr (original posts) | .bulletin (mutuals) | .humans (geography/culture) | .hazard (cognitohazards) | .realanimals (treating objects like animals/plants) | .bobbin (fashions/style, because I have none) | .diskdrive (stuff I made)
Diskdrive Subdirectory:
.starburn (clowncar of shame for elemental dimension-hopping teenagers) | .skeletonbearincident (why birth demigods when you can just bless a mortal and work with them) | .arkairia (managing your Prince's dating life when magic sex changes both real and exploitable as a political tool) | .lye (turning magic-incident resolution into an office job doesn't help the mortality rate) | .syndicatism (somehow, all magic users are either in organised crime or know someone who is) | .irlcomic (records of real life humour and stupidity)
I see a lot of Tumblr users who don't know their feminist history attributing the fact that women* are "allowed" to be masculine under the patriarchy as some sort of facet of the patriarchy universally accepting masculinity over femininity and that shit pisses me off so bad.
The reason why women* are "allowed" to be masculine when men* "aren't" allowed to be feminine under patriarchy is because DYKES and BUTCHES and other masculine women* SPENT DECADES FIGHTING FOR THEIR RIGHTS TO EXIST PUBLICLY.
The fact that women* can wear pants and suits etc. and are not constantly forced to be hyper feminine/in adherence with strict gendered dress codes without punishment (AND ONLY IN SOME COUNTRIES!) is a win on behalf of feminist political action NOT because of some baseline acceptance of masculinity in everybody by the patriarchy.
(*and people forcibly socially classed as women and men)
Like oh my lord some of you need to shut the fuck up and learn what it was like to be socially classed as a woman before the sexual revolution and what it continues to be like outside of the imperial core.
You are reaping the benefits of the activists who have come before you but because you do not know your history you are treating it like the boons of the oppressor classes and you are blind for it.
It's also the trans masc binder availability thing for me too.
I see people going "well trans men can easily get a binder" and like. Okay yes A: nowadays and B: potentially not anymore under this current administration. The reason binders are so accessible for trans mascs is because *trans mascs* got tired of the shitty options and made their own binder designs and binder companies and then dedicated to selling them at the lowest price to be able to stay in business but still be accessible in pricing for the most part. This isn't a boon that cis people have granted trans mascs, it's something trans mascs made for ourselves! And now with these new FDA warnings it's exceptionally clear that it was always on thin ice!
I'll also add on that note, as someone who was forced to wait until adulthood to come out, that binders STILL aren't accessible to many people. My first binder had to be mailed to a friend whose family was accepting, because it was not safe to mail to my own house.
And as someone raised Southern Baptist and Mennonite, there are still groups in the United States who do not even allow women to wear pants and the consequences of "crossdressing" are completely equal for both (religiously recognized) genders, since it is only socially punishable and perfectly legal for anyone to dress in any clothes-- thanks to feminism!
Giving examples, anecdotes etc. of things you have personally experienced and how you believe your various identities contribute to it
Finding community with people who have similar experiences and engaging in activism around that
What “talking about your lived experience” IS NOT:
Saying that because you have one experience, it must mean that others that aren’t like you don’t have that experience
Dictating what other groups experience in general, especially marginalized groups you don’t belong to
Invalidating other groups’ experiences as not as bad or even nonexistent in comparison to your own
Extrapolating out your personal experiences to every member of your group, regardless of other intersecting identities
Declaring that anyone who does not have your particular experience is not really a member of your group, even if you personally can’t understand how someone in your demographic doesn’t experience the same
Look I love unconditional devotion love stories as much as the next person, but there's really something so deliciously raw about conditional devotion.
I have served you and I have loved you for decades, but I will not give up my principles for you. You cut out part of my heart and took it with you down that path that you insist on walking, but you walk it alone. Even when the bleeding, gaping hole you left in my chest kills me, I will not follow you.
maybe this is not my place to say because I am monolingual, and I'm sure it's part of a larger, more nuanced discussion about visibility and accessibility on the internet, but I think it'd be cool if people posted in their native languages more instead of in english. I see people do it way more on other platforms than on tumblr which is almost exclusively in english
El problema es, como bien has dicho, la accesibilidad y la visibilidad.
Tumblr en concreto es muy anglocentrista y un gran número de los usuarios no habla más que inglés. Si quieres que tus cosas lleguen a gente con gustos u opiniones similares, escribirlo en inglés asegura que la gente por lo menos lo pueda leer. Suma a esto el hecho de que bastantes series y tal son originalmente de habla inglesa (y a veces ni se traducen a tu lengua madre), lo que crea un fandom principalmente angloparlante.
Más allá de eso, también hay que tener en cuenta las diferencias culturales que surgen entre fandoms de distintos idiomas. Por ejemplo, durante mucho tiempo el fandom de Vocaloid angloparlante y el hispanohablante han chocado con respecto a temas como la piratería. En ocasiones es complicado manejar estas expectativas, y si sabes varios idiomas, peor incluso.
A mí me gustaría subir cositas en español y encontrar a gente que comparta mis gustos, pero en Tumblr en concreto es casi imposible. Tumblr ya es de por sí mucho más «nicho» en espacios hispanohablantes que otras RRSS como TikTok o Instagram, y si tus intereses no son muy populares, despídete.
La lingüística de los espacios de fans también está hipercentrada en el inglés. No es una pareja, es un ship; no es un universo alternativo, es un AU; no es destripar, es hacer spoiler, etc. Incluso las siglas: en español es LGTB, pero lo que sueles ver es LGBT. Parece una tontería, pero esta disonancia cognitiva hace que resulte muchísimo más complicado hablar en tu propio idioma en un fandom. Por no hablar de las innumerables referencias a posts o a memes... en inglés todo, por supuesto. Como te atrevas a hacer cualquier referencia cultural no inglesa, no te entiende nadie. Pierde la gracia.
Casi todo esto se puede achacar al imperialismo cultural estadounidense. El inglés es útil para comunicarse con gente de todo el mundo, pero su omnipresencia sirve de barrera para todos los demás idiomas. Quizás habría que reflexionar un poco sobre por qué coño el resto del mundo tiene que tragarse años de clases de inglés para hablar del juego que le gusta en una red social mientras muchos angloparlantes no se dignan ni a meter un texto en un traductor automático y prefieren pasar de largo.
The longer I think on it the more I'm forced to admit that my Pokémon taste is extremely Basic Bitch, just in like three or four different directions instead of one
My favorite type of mind control is when the character only changes Just Enough to be evil/suit the controller's means
Like an excitable and playful character additionally becoming sadistic, playing around with their foes
Or an amplification of an existing trait, like a combative character becoming bloodthirsty
It's especially fun (and/or angst-worthy) the changes aren't immediately noticable
Like a character that's:
Smart
Not expressive
Affectionate, but only a few figured out how they show love
HEHEHEHEHE YEEEESSSS…GIGGLES GIDDILY it is funnest thing ever when they are still in part recognizable . aspects of the personality are retained but twisted into the wrong shape ☺️ i love when a character's self is not fully superseded but merely set off balance . go my freaking Beloved Mind Control
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
The whole "Elvis sighting" thing is hilarious because, like, the first documented career Elvis impersonators began working over twenty years before the guy even died. I wonder why a public figure who has a whole industry of people who look and sound like him would generate an unusual number of posthumous sightings? It Is A Mystery.
