words of affirmation i repeat on the daily
If someone thinks I'm annoying they are welcome to get the heck out.
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@queenelvendork
words of affirmation i repeat on the daily
If someone thinks I'm annoying they are welcome to get the heck out.

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The christian family in these memes (which are absolutely all over facebook these days) genuinely do always look miserable. Who the fuck is relating to these stock mormon farm cultists. That is a couple who made love only once in pitch darkness with bags on their heads then celebrated the pregnancy with a feast of uncooked potatoes and warm tapwater. The baby seems intrigued though. Maybe only by the bottle of pills??
Could not leave this in the tags <3
The problem with studying the deep ocean is that humans need light to look at things, the depths of the ocean are extremely dark, and what lives there is accustomed to spending most of its time in that darkness. So when we go down there with submersibles and turn on Big Lights to see, we invariably and dramatically alter what's going on, in the same way that it's generally difficult to observe the natural behaviors of terrestrial animals if you whip out a megaphone and shout HEY GUYS WHAT ARE YOU DOING at them first.
A humble snubnose eelpout on its way to the whale fall buffet when some nearby humans give it a quick, unintrusive study:
I put this in the comments but feel it needs a reblog- Check out some of Dr Edith Widderâs work on light in the deep sea! Among other things, she used the bioluminescence of stoplight fish to deduce wavelengths which most deep sea animals canât perceive and used that to create light filters to be able to film with minimal disturbance! And thatâs how we got 25 minutes of giant squid footage!!!!
and this is why baseball is the best sport (see also: these baseball sidequests)
how could you post all this and not include that Matt Hilton (bee guy) got a baseball card out of this event
and if i said nolan's odyssey starring no greek actors and with no recognizable aspects of greek culture or involvement by greeks, is the direct legacy of white supremacist colonialism that treated ancient greece as not just the pinnacle of ancient culture, but of an artificially created "european" culture, which white western europeans and their settler descendants, as the new pinnacle of culture, were the sole spiritual inheritors of.
^^^ PEOPLE ARE STILL THERE. There's a metro station across the street from the colosseum where i found a hair in my pizza slice. We drove by ruins of an amphitheater next to a motorway in greece once. It's literally just real places where real people live and have lived. It's not mythical perfect lands that once existed. I went to Itacha in 2023 and there was not enough parking space.

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I disabled this feature and was actually surprised by how much faster my laptop became. My computer is only a couple of years old but this one feature was just sucking so much life out of it behind the scenes. I'd hate to see how much an older computer would be affected. AI features that we don't want are just the absolute worst. They don't make anything better. It was actively making my computer run slower and hotter.
they had 19 year old /pol/ users going through all federal spending and deleting anything where the words were too big to understand
some people seem to think aotearoa new zealand is just fields and hills and grass. maybe some mountains. maybe a fjord or two. which is so fascinating to me because we have so many different biomes and landscapes it's bonkers.
we have massive volcanic fields. we have native palm trees and mangroves up north. we have huge swaths of geothermal areas that have geysers and bubbling hot mud and multicoloured pools. we have dense temperate rainforests. we have fern trees that grow ridiculously tall. we have huge tussock grasslands. we have a freezing cold desert. we have huge black sand beaches. we have sand dunes that stretch for kilometres. I love you aotearoa. this isn't even all of it these are just a few examples man
it's ridiculously beautiful and varied over here and yet when you say "new zealand", a lot of people imagine grass fields and sheep. which to be fair. we do also have. BUT STILL
long story short: it's not all fields and sheep. p.s. yes those are people at the top of the sand dune in the last photo
tags by @sufficientlylargen endorsed:
#I have a suspicion that a lot of people think aotearoa is a lot smaller than it actually is #I think there's a tendency especially for europeans and east-coast americans to think of it as 'a small island off of australia' #which like #it's small compared to australia #but most countries are small compared to australia #aotearoa has a greater area than the entire united kingdom #it also spans about 15° of latitude #which is 50% more than england #and also more than any US state except Alaska
"off of australia" is also a bit misleading; the distance looks small on a map, but that's because your brain is comparing it to the entire pacific ocean on the other side.
sydney (eastern coast of australia) to auckland (nz north island) is a three hour plane flight.
EXACTLY!!!!! we stretch from florida to new york if you put aotearoa on a map of the US. it's like saying "japan is a small island off the coast of china" to me
BIG!
Obligatory truck I donât trust reblog
me making ocs when i was 13: okay i want my character to be cool and different but not too different... i dont want anyone to call them a mary sue... or too edgy... or forced diverrsity.... and i have to draw good or else i could end up on a deviantart youtube video like my friend did that one time... this is hard...
me writing characters now: hes a wolf with blue hair and he has a chainsaw that can turn into a bass guitar. his name is road boy because three bikers found him on the road during the apocolypse and raised him to do stunt motorcycle tricks but they have no idea what his real name is hes bi and ace and his best friend is a trans woman and he lives in an abandoned mall

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One time a friend told me that if she wanted to have a chill night she would come to me and ask for tea and a book to read. I didnât like tea at the time, but I always made sure my cupboards had them in case she needed a quiet night. One time I told my boss that I loved oranges, but couldnât peel them because of my nails. For a year he made sure to peel me one at least once a week. Once my friends gave me a made up superlative of âmost likely to have a pen they could borrowâ and ever since Iâve made sure I always carry a pen with me. A long time ago, my high school librarian told me that no one would care what my grade in my sophomore chemistry class was if Iâm bringing them doughnuts and asking them about their day.
Sometimes friendship is about carrying pens and peeling oranges. But the point is, surrounding yourself with people who you want to do the little things for. The point of it all is bringing in the doughnuts because youâve found the people who deserve the doughnuts.
How sweet it is to be with people you enjoy taking care of
& that 1% regret rate is almost entirely âYes Iâm still trans but the surgery was bad, or the transphobia i encounter is so much worse than anticipated, or I was pushed towards a specific treatment by my binary-oriented doctor when I wanted a non-binary transitionâ etc.
Actual âwhoops, I donât identify as trans anymoreâ cases are closer to 0,02%.
people absolutely get to detransition and retransition and whatever (I personally know about six people who âdetransitionedâ from being binary trans people, and then transitioned again later as nonbinary people, for example) but the idea that a small number of people going âoops got this wrongâ somehow justifies gatekeeping everyone else is criminal, and mostly a deliberate ploy to block us from getting what we need
[ID: A tweet responding to V @ Arcless Arc @EVeracite. The tweet reads, âI like how in the context of trans affirming care, successful treatment in 99% of cases is treated as dangerous, whereas in all other areas of healthcare a 99% success rate would be treated as an absolute miracle.â.
The reply from Aster @aster_disaster_ reads, âHaving a child has a 7% regret rate. A knee replacement has anywhere between 6-30% regret. Across all types of surgery, the regret rate is 14%. Transition and trans related surgeries have a 1% regret rate.â End ID]
requested by anonymous:
RATING: RELIABLE
The statistics given are generally reliable, although of course different studies do give varying results.
The regret rate for having children in the US has been estimated at 7%. A survey in the UK found the regret rate to be at 8%.
The reported regret rate for total knee replacement varies. I have linked to two studies, one finding 17.1% of patients reported moderate or severe decision regret, the other finding a dissatisfaction rate of 8%.
Source: âTKA patients reported moderate or severe (Mod/Sev) DR [17.1% (56/328)]âÂ
Source:Â âIn an analysis of dissatisfaction after primary total knee replacement, 8% of the sample were dissatisfied at a mean follow-up of 37 months.â
The regret rate for surgery in general is 14.4%
Source:Â âSelf-reported patient regret was relatively uncommon with an average prevalence across studies of 14.4%â
The regret rate of trans related surgery is found to be around 1%. The most common reason for regret was lack of social acceptance.
Source:Â âA total of 27 studies, pooling 7928 transgender patients who underwent any type of GAS, were included. The pooled prevalence of regret after GAS was 1%
[âŚ] Overall, the most common reason for regret was psychosocial circumstances, particularly due to difficulties generated by return to society with the new gender in both social and family enviroments.23,29,32,33,36,44 In fact, some patients opted to reverse their gender role to achieve social acceptance, receive better salaries, and preserve relatives and friends relationships.â
Drone incursions are becoming more and more of an issue when it comes to wildfires, so last year I designed these graphics, but I never got around to sharing them anywhere.
If you send your drone up during a wildfire, even if you don't feel like you are that close, all planes and helicopters in the area may have to be grounded which can have a massive and detrimental effect on first responders' ability to manage that fire.
If you send your drone up and a plane or helicopter hits it, no matter how small your drone is, it could crash that aircraft injuring or killing those on board and potentially people on the ground as well. Even if the aircraft stays up, it will have damage that could ground it for an indeterminate and potentially extensive amount of time, meaning one less resource is available for that fire and other fires.
Just don't do it.
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If you want to check out the other disaster and disaster education resources I have available, visit my website.
fixed it
Tumblr Sexyman Contest 2026 Final Round
Senshi (Dungeon Meshi)
Ryland Grace (Project Hail Mary)
Mr. Ant Tenna (Deltarune)
Tenna art by @9Aaaalt29 on twt
just gonna leave these here
oh and this fan art
1 [https://danbooru.donmai.us/posts/7123389]
2 [https://danbooru.donmai.us/posts/7204896]

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You really start to understand that the âangry black womanâ and âmean lesbianâ stereotype only exists to shame us for the fact we get reasonably upset about people wanting to horrifically abuse us indiscriminately. Letâs get meaner !
Truncated text of tweet from MrPitBull, Mar 11, 2026:
She kept finding women in laboratory photographs from the 1800s. Then she read the published papersâand every single woman had vanished. Someone had erased them from history.
Yale University, 1969.
Margaret Rossiter was a graduate student studying the history of science. She was one of very few women in her program.
Every Friday afternoon, students and faculty gathered for beers and informal conversation. One week, Margaret asked a simple question: "Were there ever any women scientists?"
The faculty answered firmly: No.
Someone mentioned Marie Curie. The group dismissed itâher husband Pierre really deserved the credit.
Margaret didn't argue. But she also didn't believe them.
So she started looking.
She found a reference book called "American Men of Science"âessentially a Who's Who of scientific achievement. Despite the title, she was shocked to discover it contained entries about women. Botanists trained at Wellesley. Geologists from Vermont.
There were names. There were credentials. There were careers.
The professors had been wrong.
But Margaret's discovery was just the beginning. Because as she dug deeper into archives across the country, she found something far more disturbing.
Photograph after photograph showed women standing at laboratory benches, working with equipment, listed on research teams.
But when she read the published papers, the award citations, the official historiesâthose same women had disappeared. Their names were missing. Their contributions erased.
It wasn't random. It was systematic.
Women who designed experiments watched male colleagues publish results without giving them credit. Women whose discoveries were assigned to supervisors. Women listed in acknowledgments instead of as authors. Women passed over for awards that went to male collaborators who contributed far less.
Margaret realized she was witnessing a pattern that stretched across centuries.
Women had always been present in science. The record had simply pushed them aside.
She needed a name for what she was documenting.
In the early 1990s, she found it in the work of Matilda Joslyn Gageâa 19th-century suffragist who had written about this exact phenomenon in 1870.
In 1993, Margaret published a paper formally naming it: The Matilda Effect.
The term captured something that had been hidden in plain sight for generations. Once you knew the term, you saw it everywhere.
Her dissertation became a lifelong mission.
For more than 30 years, Margaret researched and wrote her landmark three-volume series: Women Scientists in America. She examined letters, institutional policies, individual careers. She gathered undeniable evidence that women in science had been consistently under-credited and structurally excluded.
Her work faced resistance. Many dismissed women's history as political rather than academic. Others insisted she was exaggerating.
Margaret didn't argue emotionally. She presented data. Documented cases. Patterns repeated across decades and institutions.
Eventually, the evidence became undeniable.
Her research helped restore recognition to scientists who had been erased:
Rosalind Franklin, whose X-ray work revealed DNA's structureâcredit went to Watson and Crick.
Lise Meitner, who explained nuclear fissionâomitted from the Nobel Prize.
Nettie Stevens, who discovered sex chromosomesâreceived little credit.
Cecilia Payne-Gaposchkin, who discovered stars are made of hydrogenâinitially dismissed.
And countless others whose names had nearly vanished.
Margaret changed the narrative. Science was no longer just the story of solitary male geniuses. It became a story of collaboration that included women who had been written out.
The Matilda Effect became standard terminology. Scholars used it to examine how credit is assigned, how authors are listed, who receives awards, who gets left out.
This is an important concept, but the piece is written by AI.
There are a number of tells, but this is an excellent example to talk about em-dashes, which people often either take as permanent AI tells or run the other way and say "humans use em-dashes and that's why AI does, too! they're not tells!" Both are kind of right and both are kind of wrong.
What you'll see if you look closely at this text is that it ONLY uses em-dashes. Every time it needs to put in some kind of break or set off some text, it goes for the em-dash. There are no phrases in parentheses. There are commas, but only in places where the absolute rule is to use a comma (like in a series, for instance). There is one colon, again placed where the absolute rule is to use at (at the top of a list). Whenever there's an option, where a human writer would be actively making a choice about what punctuation to use, the AI defaults to an em-dash.
On top of that, look at the content. The AI bot people are obsessed with feminism, ironically. I suspect it's because very basic feminist narratives about women pushing back against barriers or doing something heroic are popular and gets shared widely. So, first of all, you should be on your guard when you see a "what this woman did CHANGED HISTORY!" kind of piece. (I wonder if the twitter/tumblr trend of BUCKLE UP history posts has affected the AI ...) And then you should check out the specific claims.
She kept finding women in laboratory photographs from the 1800s. Then she read the published papersâand every single woman had vanished. Someone had erased them from history.
I can't find this anywhere else. The paper "The Matthew Matilda Effect in Science" doesn't talk about photos! The Wikipedia page doesn't talk about photos! This Smithsonian article doesn't talk about photos! Her piece on her career in Writing and Revising the Disciplines (2002) (good read) DOES mention photos, in that she got the Mount Holyoke archivist to send her a few from the 1880s showing women doing scientific work as a nice illustration that "epitomized" what she was already aware of.
Rossiter started with textual primary sources that documented women as named individuals contributing to scientific discoveries. The idea of her being confused by photos is a hallucination.
Despite the title, she was shocked to discover it contained entries about women. Botanists trained at Wellesley. Geologists from Vermont.
There's definitely something to be said about the framing of this bit as shocking!!! but since I'm talking about facts and sources, it's clear to me that the AI recognized the botany-Wellesley connection from the paper but could not parse that the reference was to a female botanist who taught at Wellesley. There is also nothing in the paper about Vermont geologists, so I have no idea where the AI got that; I would suspect it's another hallucination attempting to create a pattern from the first reference.
But Margaret's discovery was just the beginning. Because as she dug deeper into archives across the country, she found something far more disturbing. Photograph after photograph showed women standing at laboratory benches, working with equipment, listed on research teams. But when she read the published papers, the award citations, the official historiesâthose same women had disappeared. Their names were missing. Their contributions erased.
Again, back to the mysterious photographs. But the rest of this text is an issue as well: what Rossiter describes in the paper is not a complete absence of these women in any official documentation, but that these women were amply documented and known to be working within the scientific community and yet did not receive public credit or awards. It's not a complete smothering out, but a sort of complacent back-burnering, which is too nuanced for the AI to be able to handle when told to "write a post about the Matilda effect that will get engagement on social media". She didn't prove that discoveries attributed to male authorship actually had women involved and only she knew their names: she collected many stories that people already knew of overlooked/underplayed female scientists and put them together to say, "This is a pattern and we should have a name for it." Some of her examples were even recent enough (1970s-80s) that she was able to point to a feminist backlash.
And again ironically, the AI itself engages in the Matilda Effect by presenting this whole thing as utter silence -> Rossiter gets curious -> the case is blown open. Rossiter actually refers to the work of other female historians and social scientists! In fact, she started this line of research after noticing the female biographies in American Men of Science when her housemate, Cynthia Thompson, recommended that she keep track of them.
Her research helped restore recognition to scientists who had been erased: Rosalind Franklin, whose X-ray work revealed DNA's structureâcredit went to Watson and Crick. Lise Meitner, who explained nuclear fissionâomitted from the Nobel Prize. Nettie Stevens, who discovered sex chromosomesâreceived little credit. Cecilia Payne-Gaposchkin, who discovered stars are made of hydrogenâinitially dismissed. And countless others whose names had nearly vanished.
Rossiter did not claim to be rediscovering these women. She refers to Franklin and Meitner as having been famously denied credit, in fact! Meitner specifically is "one of the best-known examples of the phenomenon". Stevens she uses as one in the list of examples in the paper, and Payne-Gaposchkin actually just gets a reference at the end that's doesn't even tell you the specific field of scientific study. (To be fair, there may be more about them in her other publications.) This was not about Badass Historian of Science Tells the Establishment What's What. Everybody knew about the concept of female scientists being publicly ignored as collaborators by 1993 â and women's history as a field had been around for 15-20 years. She was not working in a vacuum where nobody else thought that it was important to study these topics until she forced them to see the light.
Please, please, everyone, be on the lookout for bad feminist history written by AI. If you're not with me on the tells and hallucinations here, then at least be on the lookout for bad "feminist" history regardless of the source. If it sounds like it's sensationalizing, it probably is.
it seems like insult to injury on the photographic point to note that the photo from this tweet is not in fact Margaret Rossiter (picture of her below):
but a different missing scientist that doesn't appear in the text of the tweets, Dr. Jocelyn Bell Burnell.
also, I think it's fascinating (read: typical, disappointing) that not a single one of the scientists mentioned in the LLM content wasn't white. Like say, Marie Maynard Daly, who did pioneering work in heart disease and cigarette smoking:
Jewel Plumber Cobb, one of the first to study what would later be termed "precision medicine" or how different people respond differently to chemotherapy in oncology:
or Chien-Shiung Wu, experimental physicist and Manhattan Project contributor.
and lest anyone think I had to dig hard for this information somewhere obscure, all three of these examples are from a single article in Smithsonian magazine, on the first page of results in DuckDuckGo (non-AI version). Literally less than a minute to find.
I don't mean to shame people using LLMs because they don't trust their own abilities. But if you're out there doing that I want you to know there is nothing about them smarter or better than YOU and YOUR BRAIN because LLMs can't question themselves. They're very large magic 8 balls that can't generate new content, only thoughts someone else has already had. So if people out there are making obvious mistakes based on bias and you use LLM trained with that (read: all of it other than a few very carefully curated and proprietary models not the ones easily there for consumer use) you ARE going to repeat those mistakes. There's no way to stop it.