通称
つうしょう
common name; popular name; nickname; name commonly used
この会社は通称「JR」と呼ばれています。 この かいしゃ は つうしょう 「JR」 と よばれています。 This company is commonly known as "JR."
One Nice Bug Per Day
2025 on Tumblr: Trends That Defined the Year
Monterey Bay Aquarium
KIROKAZE
ojovivo

shark vs the universe
Jules of Nature
occasionally subtle

Origami Around

roma★
let's talk about Bridgerton tea, my ask is open
TMBGareOK. The Official They Might Be Giants tumblr
RMH

ellievsbear
h

pixel skylines
Mike Driver
Fai_Ryy
No title available

PR's Tumblrdome

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@antuntunum
通称
つうしょう
common name; popular name; nickname; name commonly used
この会社は通称「JR」と呼ばれています。 この かいしゃ は つうしょう 「JR」 と よばれています。 This company is commonly known as "JR."
Kyle Gordon's back
whenever you get frustrated with Japanese bc it has way too many freaking homophones, just remember, you are exactly right
source: https://selftaughtjapanese.com/2014/03/19/research-results-homophones-in-japanese/
Research Results: Homophones in Japanese – Self Taught Japanese
How to say “AND” in Japanese
Saying AND in Japanese has always been kind of frustrating for me. It’s a concept that, in English (and in my mother tongue, Italian), feels simple and universal: you just use “and” and move on.
But in Japanese, you need to stop and think: what exactly am I connecting, and what do I actually want to say?
It’s been a long time since my last post and since my last serious effort to make a significant improvement in my Japanese, so let’s start again by breaking down something that has always confused me.
1. Connecting nouns
と = “and” (complete list)
Use と when you are listing specific things and the list is complete.
Examples:
猫と犬がいます。 = I have a cat and a dog.
日本語と英語を話します。 = I speak Japanese and English.
や = “A, B, and things like that” (incomplete list)
Use や when you mention examples but there are more things that are not listed.
Examples:
猫や犬が好きです。 = I like cats, dogs, and things like that.
本や映画を見る。 = I read books, watch movies, and do things like that.
とか = “things like A and B / for example”
とか is more casual and flexible than や.
Examples:
映画とか音楽が好きです。 = I like things like movies and music.
週末はゲームをするとか、友達と会うとかします。 = On weekends, I do things like play games or meet friends.
It can also show uncertainty:
明日とか? = Maybe tomorrow or something?
2. Connecting adjectives
い-adjectives → くて
Remove the final い and add くて.
Examples:
安い → 安くて = cheap and...
この店は安くておいしいです。 = This shop is cheap and delicious.
な-adjectives → で
Add で.
Examples:
静か → 静かで = quiet and...
この町は静かで便利です。 = This town is quiet and convenient.
Mixing い-adjectives and な-adjectives
The rule stays the same: change the first adjective into its connecting form.
Examples:
安くて便利 = cheap and convenient
静かで面白い = quiet and interesting
3. Connecting verbs
て-form = “do A and then B / do A and B”
The て-form is the basic way to connect actions.
Examples:
起きて、顔を洗う。 = Wake up and wash my face.
ご飯を食べて、学校へ行く。 = Eat and go to school.
たり〜たりする = “do things like A and B”
Use this when listing examples of actions, not a complete list.
Structure:
Verb た-form + り + Verb た-form + り + する
Examples:
映画を見たり、本を読んだりします。 = I do things like watch movies and read books.
週末は寝たり、ゲームをしたりします。 = On weekends, I do things like sleep and play games.
4. Connecting phrases
そして = and / then
Example:
日本へ行きました。そして、日本語を勉強しました。 = I went to Japan. Then, I studied Japanese.
だから = so / therefore
Example:
雨です。だから、家にいます。 = It’s raining, so I’m staying home.
The important thing to understand is that Japanese doesn’t really have one single word for "and". Instead, it asks you: what kind of relationship do these two things have? And once you start thinking that way, it becomes much easier.
梅雨の単語
Some vocabulary words for the rainy season in Japan.
梅雨 つゆ rainy season
梅雨入り つゆいり the beginning of the rainy season
梅雨明け つゆあけ the end of the rainy season
長雨 ながあめ prolonged rain; continuous rainfall
小雨 こさめ light rain; drizzle
土砂降り どしゃぶり torrential rain; downpour
豪雨 ごうう heavy rain; torrential rainfall
湿気 しっけ humidity; moisture in the air
蒸し暑い むしあつい hot and humid; muggy
曇り空 くもりぞら cloudy sky
水たまり みずたまり puddle
雨具 あまぐ rain gear (umbrella, raincoat, etc.)
五月雨 さみだれ early summer rain; an old poetic word for the rainy season
入梅 にゅうばい the beginning of the rainy season (formal/meteorological term)
空梅雨 からつゆ a dry rainy season with little rainfall
梅雨前線 ばいうぜんせん Baiu front; seasonal rain front
雨宿り あまやどり taking shelter from the rain
じめじめする to be damp and humid
しとしと降る しとしとふる to rain softly and steadily
ザーザー降る ザーザーふる to rain heavily
Which shape is bouba and which is kiki? A Lingthusiasm zine
Here are two shapes. If you had to assign the name "bouba" to one of them and "kiki" to the other, which name would you give to which? If you think about it, you probably have a guess… In this Lingthusiasm zine, Gretchen McCulloch gets enthusiastic about these two linguistically famous shapes.
Print and fold your own copies for free!
8½ x 11 paper: colour, b&w
A4 paper: colour, b&w
Learn more about bouba and kiki!
What words sound spiky across languages? (Lingthusiasm podcast)
Which is "bouba" and which is "kiki"? (Tom Scott Language Files video)
Bouba/Kiki Effect (Wikipedia)
Research cited
Cwiek et al 2021, Novel vocalizations are understood across cultures
Loconsole et al 2026, Matching sounds to shapes: Evidence of the bouba-kiki effect in naïve baby chicks
Öztürk et al 2012, Sound symbolism in infancy: Evidence for sound–shape cross-modal correspondences in 4-month-olds
Styles and Gawne 2017, When does maluma/takete fail? Two key failures and a meta-analysis suggest that phonology and phonotactics matter
More from Lingthusiasm
More zines that are enthusiastic about linguistics!
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Chinese wushu technique, shuang-jie-gun. (CR 楚儿霸王)
Fluent Forever's 625 must know words
in your target language
Animal: dog, cat, fish, bird, cow, pig, mouse, horse, wing, animal
Transportation: train, plane, car, truck, bicycle, bus, boat, ship, tire, gasoline, engine, (train) ticket, transportation
Location: city, house, apartment, street/road, airport, train station, bridge, hotel, restaurant, farm, court, school, office, room, town, university, club, bar, park, camp, store/shop, theater, library, hospital, church, market, country (USA, France, etc.), building, ground, space (outer space), bank, location
Clothing: hat, dress, suit, skirt, shirt, T-shirt, pants, shoes, pocket, coat, stain, clothing
Color: red, green, blue (light/dark), yellow, brown, pink, orange, black, white, gray, color
People: son, daughter, mother, father, parent (= mother/father), baby, man, woman, brother, sister, family, grandfather, grandmother, husband, wife, king, queen, president, neighbor, boy, girl, child (= boy/girl), adult (= man/woman), human (≠ animal), friend (Add a friend’s name), victim, player, fan, crowd, person
Job: Teacher, student, lawyer, doctor, patient, waiter, secretary, priest, police, army, soldier, artist, author, manager, reporter, actor, job
Society: religion, heaven, hell, death, medicine, money, dollar, bill, marriage, wedding, team, race (ethnicity), sex (the act), sex (gender), murder, prison, technology, energy, war, peace, attack, election, magazine, newspaper, poison, gun, sport, race (sport), exercise, ball, game, price, contract, drug, sign, science, God
Art: band, song, instrument (musical), music, movie, art
Beverages: coffee, tea, wine, beer, juice, water, milk, beverage
Food: egg, cheese, bread, soup, cake, chicken, pork, beef, apple, banana, orange, lemon, corn, rice, oil, seed, knife, spoon, fork, plate, cup, breakfast, lunch, dinner, sugar, salt, bottle, food
Home: table, chair, bed, dream, window, door, bedroom, kitchen, bathroom, pencil, pen, photograph, soap, book, page, key, paint, letter, note, wall, paper, floor, ceiling, roof, pool, lock, telephone, garden, yard, needle, bag, box, gift, card, ring, tool
Electronics: clock, lamp, fan, cell phone, network, computer, program (computer), laptop, screen, camera, television, radio
Body: head, neck, face, beard, hair, eye, mouth, lip, nose, tooth, ear, tear (drop), tongue, back, toe, finger, foot, hand, leg, arm, shoulder, heart, blood, brain, knee, sweat, disease, bone, voice, skin, body
Nature: sea, ocean, river, mountain, rain, snow, tree, sun, moon, world, Earth, forest, sky, plant, wind, soil/earth, flower, valley, root, lake, star, grass, leaf, air, sand, beach, wave, fire, ice, island, hill, heat, nature
Materials: glass, metal, plastic, wood, stone, diamond, clay, dust, gold, copper, silver, material
Math/Measurements: meter, centimeter, kilogram, inch, foot, pound, half, circle, square, temperature, date, weight, edge, corner
Misc Nouns: map, dot, consonant, vowel, light, sound, yes, no, piece, pain, injury, hole, image, pattern, noun, verb, adjective
Directions: top, bottom, side, front, back, outside, inside, up, down, left, right, straight, north, south, east, west, direction
Seasons: Summer, Spring, Winter, Fall, season
Numbers: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 30, 31, 32, 40, 41, 42, 50, 51, 52, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 91, 92, 100, 101, 102, 110, 111, 1000, 1001, 10000, 100000, million, billion, 1st, 2nd, 3rd, 4th, 5th, number
Months: January, February, March, April, May, June, July, August, September, October, November, December
Days of the week: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
Time: year, month, week, day, hour, minute, second , morning, afternoon, evening, night, time
Verbs: work, play, walk, run, drive, fly, swim, go, stop, follow, think, speak/say, eat, drink, kill, die, smile, laugh, cry, buy, pay, sell, shoot(a gun), learn, jump, smell, hear (a sound), listen (music), taste, touch, see (a bird), watch (TV), kiss, burn, melt, dig, explode, sit, stand, love, pass by, cut, fight, lie down, dance, sleep, wake up, sing, count, marry, pray, win, lose, mix/stir, bend, wash, cook, open, close, write, call, turn, build, teach, grow, draw, feed, catch, throw, clean, find, fall, push, pull, carry, break, wear, hang, shake, sign, beat, lift
Adjectives: long, short (long), tall, short (vs tall), wide, narrow, big/large, small/little, slow, fast, hot, cold, warm, cool, new, old (new), young, old (young), good, bad, wet, dry, sick, healthy, loud, quiet, happy, sad, beautiful, ugly, deaf, blind, nice, mean, rich, poor, thick, thin, expensive, cheap, flat, curved, male, female, tight, loose, high, low, soft, hard, deep, shallow, clean, dirty, strong, weak, dead, alive, heavy, light (heavy), dark, light (dark), nuclear, famous
Pronouns: I, you (singular), he, she, it, we, you (plural, as in “y’all”), they.
Natural Scenery / 自然景観
Some words to describe natural scenery
自然景観 しぜんけいかん natural scenery
谷 たに valley
峡谷 きょうこく canyon; gorge
渓谷 けいこく valley; ravine; gorge
崖 がけ cliff; precipice
断崖 だんがい cliff; sheer precipice
平野 へいや plain; flatland
草原 そうげん grassland; prairie; meadow
丘 おか hill; small hill
山脈 さんみゃく
滝 たき waterfall
海岸 かいがん coast; shoreline
岩 いわ rock; boulder
岩壁 がんぺき rock wall; cliff face
洞窟 どうくつ cave
火山 かざん volcano
湿原 しつげん marsh; wetland
砂丘 さきゅう sand dune
盆地 ぼんち basin (low area surrounded by mountains)
"Six weeks into the term, I assigned my rhetoric and writing students a 20-page article. It was the same length I had assigned for five years and the same length I had read without complaint as an undergraduate a decade ago. Not one student finished it.
When I asked why, a student answered honestly: It was too long, and she kept losing track of what the paper was about. This was not a remedial class: These were students who had cleared the admissions process and written essays good enough to get them here. Yet a routine academic reading assignment had defeated them.
Every generation of professors has complained that their students cannot read. The lament is usually overblown, but data have caught up to anecdote, and what I am seeing in my classroom is no longer a hunch. There is a measurable, generational collapse in sustained reading and writing, and the academy is responding to it with improvisation and exhaustion rather than the structural overhaul it requires.
In February 2024, Adam Kotsko, who teaches in the Shimer Great Books School at North Central College, wrote in Slate that students who once handled 30 pages of reading per class meeting now seem “intimidated by anything over 10 pages and seem to walk away from readings of as little as 20 pages with no real understanding.” Crucially, he added that this is “not a matter of laziness on the part of the students” but of underlying skills they were never given a chance to build.
The Chronicle of Higher Education’s 2024 investigation found the same pattern across institutions as different as the Stevens Institute of Technology and Wellesley College, where the average SAT exceeds 1400. Nicholaus Gutierrez, an assistant professor at Wellesley, told The Chronicle that the baseline for what students consider a reasonable amount of work has dropped so noticeably that he has cut his readings accordingly; a 750-word essay now strikes many students as long. At Stevens, the science and technology studies associate professor Theresa MacPhail described following the mantra of “meet your students where they are” for so long that she has begun to feel “like a cruise director organizing games of shuffleboard.”
Worse, the national data tell the same story in colder language. On the 2011 National Assessment of Educational Progress (NAEP) writing assessment, which is the most recent comprehensive writing benchmark, only 24 percent of 12th graders reached the Proficient level, and just 3 percent reached Advanced; another 21 percent scored below Basic. The reading side of the ledger is worse, and getting worse fast: The 2024 NAEP results released in September 2025 show 12th-grade reading scores at the lowest level recorded since the assessment began in 1992. Thirty-two percent of 12th graders now score below NAEP Basic in reading, meaning that, in the assessment’s own language, they likely “cannot draw general conclusions based on concepts presented explicitly in a text.” And yet more than half of these same seniors reported being accepted to a four-year college. That last sentence is the whole problem in one line: We are admitting a cohort that cannot read at a college level and are pretending otherwise.
Why is this happening? One reason, of course, is smartphones.
I came into teaching as a skeptic of the anti-smartphone argument: I had a phone in my pocket throughout high school and college in the 2010s, and I read long books anyway. I now think I was wrong, because the neuroscience has caught up. In a 2017 paper, Adrian F. Ward and colleagues at the University of Texas at Austin’s McCombs School of Business showed that the mere presence of a participant’s smartphone — whether that be face down, powered off, untouched, or across the desk out of vision — measurably reduces available working memory and fluid intelligence on cognitive tests, with the largest effects on the most phone-dependent users. A 2022 study by Motoyasu Honma and colleagues at Japan’s Showa University used near-infrared spectroscopy to compare reading on a smartphone with reading the same passage on paper, and found that smartphone reading produced overactivity in the prefrontal cortex, suppressed sigh generation, and led to general lower comprehension scores; the authors argued that the sigh inhibition and prefrontal overload were causally linked to the comprehension decline.
So when a student tells me they “kept losing track” of a 20-page article, I have to acknowledge that they may be describing a measurable neurological condition. The neural pathways that support sustained attention are built by use, and they atrophy without it. Your body is a use-it-or-lose-it system, and the brain is no exception.
Another reason for the decline in student reading capability is increasing reliance on generative AI. In June 2025, Nataliya Kosmyna and colleagues at the MIT Media Lab released a preprint titled “Your Brain on ChatGPT.” They divided 54 participants into three groups writing SAT-style essays — one using ChatGPT, the second group using a search engine, the last group using nothing — and monitored brain activity with a 32-channel EEG. The ChatGPT group showed the lowest neural connectivity of the three, with up to 55 percent reduced connectivity compared with the brain-only group, and “consistently underperformed at neural, linguistic, and behavioral levels.” Eighty-three percent of LLM users could not quote a single line from essays they had written minutes earlier. When the LLM group was forced to write without AI in a follow-up session, their brain activity did not bounce back to baseline; the researchers coined the term “cognitive debt” for the lingering deficit.
This is the first neurophysiological evidence that early reliance on LLMs measurably alters the brain’s engagement with writing tasks, and it is consistent with what those of us in front of classrooms are watching happen in real time. When I assign analysis, I am not trying to extract a polished product; I am trying to put the student’s mind through resistance in order to make it stronger. Offloading the struggle to a chatbot does not “free students up for higher-order work.” It deprives them of building the strength to do any substantial cognitive work at all.
There is a final factor that is contributing to this decline in reading skills, and that is that the students arriving in my classroom today are the first cohort to have experienced Common Core-influenced reading instruction across the entirety of their K–12 schooling. Whatever the standards’ original intent, the on-the-ground implementation in many districts replaced sustained reading with the practice of pulling “evidence” from disconnected short passages, the same format used on the standardized tests that increasingly determine school funding. The education scholar Natalie Wexler, among others, has documented this pivot in detail: Students drilled on “finding the main idea” in two-paragraph excerpts never build the stamina or background knowledge that longform reading requires. The pandemic then added fuel to a fire that was already burning. NAEP scores for 13-year-olds dropped sharply in 2022 and have not recovered. A 2023 EdWeek survey found that 24 percent of secondary-school administrators described pandemic learning loss in English and language arts as “severe or very severe.”
In July 2025, the journalist Mary Harrington argued in The New York Times that “thinking is becoming a luxury good.” The ability to read deeply and reason at length is fragmenting along class lines as ultra-processed digital media replaces text in everyday life, much as ultra-processed food has replaced cooking. Her longer treatment of the subject in First Things makes the more provocative case that we are witnessing the end of print culture itself, and with it the end of the cognitive substrate on which modern liberal democracy was built.
I see this stratification in the classroom and on the page every week. My students from districts that protected sustained reading through small class sizes, strict phone policies, and faculty who refused to teach to the test all arrive with their attention relatively intact. My students from districts that surrendered to devices and standardized testing arrive cognitively winded. A democracy that requires a literate electorate is now training one fraction of that electorate out of literacy while marketing to the other a “deep work” lifestyle as a luxury good. The students who cannot read a 20-page article today are the voters who will not be able to read a bill, or the jurors who cannot follow a closing argument, tomorrow.
I do what I can in my own classroom to address the problems. I break 20-page articles into two halves and assign the first half with explicit analytical tasks. I require exploratory writing before formal drafts. I model (visibly, on the board) how to track an argument across pages or distinguish a source’s claim from my own analysis. I make structured peer review explicit, because the workshop format I used to take for granted now collapses into “this is good” and “maybe add more details” the moment I step back.
But I want to be plain about the limits of what an individual instructor can do, and all of these solutions have costs. Scaffolding a 20-page article into halves compromises the integrity of the argument I am asking students to engage, just as modeling note-taking in a credit-bearing rhetoric course is using a college slot to teach a middle-school skill. None of the syllabi I teach are designed to deliver this type of cognitive rehabilitation, and pretending otherwise has produced credential inflation. We cannot keep conferring degrees on students who cannot do what the degree is supposed to certify.
I’m afraid I don’t have answers. I do, however, have some questions that may point us in the right direction. If higher education is going to respond to the reading crisis as a structural problem rather than a private burden carried by composition instructors and adjuncts, it has to stop avoiding the following questions: If a majority of incoming students cannot read at a level the curriculum requires, are we admitting students we cannot serve, or offering a curriculum we cannot provide?
Why are first-year writing and reading-intensive general-education courses still the most adjunctified, lowest-paid, highest-load corner of the university, at the precise moment when their work has become the most important work the institution does? What is the responsible institutional response for AI usage: Is it a syllabus statement, or a sequencing principle that requires students to demonstrate the cognitive work themselves before AI assistance is permitted?
Why are most college classrooms still phone-permissive by default? K–12 districts from Florida to California are now banning phones bell to bell; higher education has somehow lagged behind the public schools. Universities benefit from a pipeline they did not build and refuse to repair. What would it mean for a university system to invest seriously in the reading instruction happening in the high schools that feed it, rather than treating remediation as something to be quietly outsourced to first-year composition instructors?
The thing I am no longer willing to do is pretend this is a temporary adjustment period, or that “students will adapt.” They will not adapt on their own. The conditions that produced this collapse are still in place: the phones, the algorithmic feeds, the test-prep excerpts, staffing models that load the reading-intensive work onto the most precarious faculty, and now the chatbots that finish students’ sentences before they’ve even begun to think of them. If we want literate citizens, we will have to rebuild the conditions for literacy deliberately, against the grain of every incentive currently pointed the other way. I know the academy has the will to do that. It also has the obligation."
— Tyler Jagt, 1 June 2026, "My Students Can’t Read"
The generational collapse in literacy is measurable, persistent, and likely to get worse.
I want to be very clear on this: it is not just dependence on smartphones and phones causing mental atrophy. It's that for decades we have taught Three Cueing System as a way to read, and while we are now beginning to correct, it is catching up with us. The reliance on smartphones and short form video are in part a result of illiteracy, not necessarily a cause of it, because we have failed to give students the tools to actually read but also paired it with the belief that they can read. They do not seek remedial reading help but blame the materials for being unclear or too difficult, when the fundamental problem is that the more complex the text, the less functional the three cueing system is. They are often quite literally guessing what the text says by searching for words they recognize (or think they recognize but cannot verify) and texts rapidly become impenetrable nonsense. Of course people will reach for their phones when 90% of the text they encounter in their daily lives and schooling is not accessible to them!
Truncated text of tweet from MrPitBull, Mar 11, 2026:
She kept finding women in laboratory photographs from the 1800s. Then she read the published papers—and 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 it—her 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"—essentially 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 histories—those 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 Gage—a 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 structure—credit went to Watson and Crick.
Lise Meitner, who explained nuclear fission—omitted from the Nobel Prize.
Nettie Stevens, who discovered sex chromosomes—received little credit.
Cecilia Payne-Gaposchkin, who discovered stars are made of hydrogen—initially 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.
sigh. just another day scrubbing the floor and mowing the lawn and dusting and doing the laundry for the rest of my pack. but the house has to be in especially perfect shape today because Alpha Jameson has an important meeting with another Alpha from across the river. If they come to an agreement, the Newport and Cincinnati packs might finally have peace for the first time in decades. No more fighting….But they say the Newport Alpha is the most ruthless wolf who’s ever lived. Can our hotheaded Alpha really find a compromise with a man like that? I have to hope for the best…with a deal between our packs, the months of new business negotiations will have everyone so busy, they won’t have time to push me around. Alpha Jameson might even be too distracted to think about me. The thought is almost too good to be true. I’ve been his scapegoat to treat like trash ever since he and my younger sister claimed each other as mates. There was a time when we were kids when it was me on his arm at dinners and parties. But then we grew up, and…..I never got my Wolf. I’m a freak, and everyone knows it. Of course he couldn’t stay with me. Not that I’d want to be with him now anyway. These days he can’t even say my name without spitting it. Sometimes I think it would be easier if I never get my Wolf, and I get banished to live among humans. But then I remember my childhood best friend. She was so pretty—brown eyes, with brunette hair she always wore in a bun. I was homeschooled with my pack, of course, and she went to the local high school. We met at the library….our shared sanctuary. She didn’t have any other friends, and neither did I. We hung out every chance we got. Until one day when we were 16…her brother told me she was gone. I found out that their mom gave her away to a boy band, and I haven’t seen her since. That’s when I realized the human world is just as ruthless as the wolfen. No, banishment wouldn’t be better. But I don’t know how much longer I’ll survive this place either. Most days, keeping my head down and doing what I’m told isn’t enough to keep me out of trouble.
But things could be worse. Yesterday I overheard my sister talking to Beta Devon about the deal Alpha Jameson is making with the Newport Alpha. Apparently, he’s requested a woman from our pack as his mate. With his reputation, I could almost feel bad for whoever Alpha Jameson chooses for him, even though the women in our pack treat me even worse than the men. I’m an embarrassment to them because I don’t have my Wolf.
Whatever. At least I know it won’t be me, because I’m not important enough to be married off……..
everyone saying that they can hear the MC’s voice so clearly. That’s because I didn’t write this. I channeled her voice through myself as a vessel. She’s out there somewhere.
why did I say it like that? We know for a fact that she’s in Cincinnati, Ohio. 
funny you should mention it because I’m channeling the MC again right now and she met the Newport Alpha today. Her stomach was in her throat when she found out that he requested her, specifically. Whatever she’ll have to endure will almost be worth the look on Alpha Jameson’s face when he was forced to acknowledge that someone actually wants her—that someone outside of her pack even knows her name.
Still, the satisfaction was fleeting when it finally sank in that she’s leaving with the most ruthless Wolf this side of Louisville. Is she simply out of the pan and into the fire?
Not so much. In fact, the Newport Alpha is cold as ice. He hasn’t spoken a single word to her in the hour since they met and left Cincinnati on his sleek, burnt-sienna Ecosse ES1 Spirit.
Could he really have asked for her, specifically? What if he’d asked for someone else and they sent her instead, as a consolation prize? What if…
What if he asked for someone else, and they lied about who she was? Oh god. Would she have to pretend to be Payton or Sabrina to maintain peace and to keep her own head attached to its neck? She might be able to pull that off…for a week.
Does he even know what she is—what she isn’t. Did Alpha Jameson or her sister tell him she doesn’t even have her Wolf? Maybe the Alpha can sense that on his own…
They’ve stopped for gas, and he still hasn’t said a word. But he when he goes inside for an energy drink, he comes back out with sweet-tarts ropes—her favorite. It’s such a random candy too. How could he have possibly known that? A lucky guess?
They share an impossibly familiar look for just a moment as he hands her the candy. Then he’s astride the motorcycle again.
She wishes she had something other than him to hold onto as they speed southbound on 471. Despite herself, her arms are wrapped around his waist, and she tucks her forehead against his broad back so the wind won’t sting her eyes.
His carhartt jacket smells faintly of clove cigarettes. His hair smells like apricot shampoo from the dollar general. The specificity of the scent catches her off guard as they cross the bridge into Newport. Why would she recognize the brand? More importantly: why would a wealthy Alpha buy his hair products from a dollar store?
And why is she even thinking about his shampoo to begin with? She needs to be preparing herself for her first night in her new life. It could be anything. She needs to be smart. She needs to be on guard.
And yet…she can’t stop thinking about his brown eyes. Something in them is so….impossibly…..familiar. It just doesn’t make any sense.
That's very kind, but again I'm not writing this. I'm having visions and ecstasies where I see through the eyes of the MC. In fact......I'm being overcome now......
We've been driving for a long time now, well past Newport's city limits. At some point, we got off the highway, and I counted streetlights blurring by until we started passing trees instead. We're out somewhere in the woods now. I tell myself that I'll get my bearings the next time we stop, but we just drive on and on.
#reading this feels like having knives thrown at you
Well get ready to start dodging, because for the first time in a year, I can feel the MC trying to speak through me...
I awake to a crash. Or was it a scream ... My own voice, screaming.
I'm breathing hard—panting, even—my whole body too hot in the Hollister sweater I went to bed in last night. I shouldn't be surprised; it's not the first time I've screamed myself awake, but it usually only follows the times I've cried myself to sleep. Last night wasn't one of those times. No, last night was ... I can't bring myself to even think the word safe. Instead, I say out loud to the dark room, "different."
My voice is timid as always, but at least I'm speaking. Maybe, in the life I've lived, anything that's different is safe.
The MC has made a solemn vow not to make us another year, I promise.
に会う vs. と会う
I revisited this today and I just couldn't remember the difference, so writing it down.
と会う both parties make an effort to meet
に会う only one party moves
So for example: 叔母に会う。This implies that I'm going to go to my aunt's place or somewhere that I'm the person moving.
叔母と会う。We are meeting up somewhere, where both of us needs to take action.
To expand a bit more:
✅ 車が壁にぶつかる💥。
❌ 車が壁とぶつかる。
The car crashing into the wall means only the car is taking action, so using に is more natural in this case.
I like how when I type ぶつかる it let's me select the emoji, so I know exactly what it means 🤣
目を開けた vs. 目を開いた vs. 目を覚ます
And another one that is related is 眼を覚ます, which means to wake up.
目を覚ましたけど、まだ目を開けたくなかった。 I woke up, but I didn’t want to open my eyes yet.
I went to a library book sale this weekend and I found a very old book called “Electronic Life: How to Think About Computers,” which was published in I think 1975? I’ve been reading it kind of like how I would read a historical document, and it’s lowkey fascinating
There’s a whole paragraph that’s like “okay, find the keyboard. Don’t panic if it has more keys than a typewriter, that’s normal. Really, it’s fine. The extra keys don’t make things harder. It’s FINE”
Thought this section was particularly interesting:
Can the computer create something? At first glance it seems obvious that it can. Animated computer graphics, with their fluid transitions and whiplash perspectives, look strikingly new. And if one watches the machine doing animation work, there seem to be lengthy periods when the computer is acting “on its own.”
But if one observes these processes in more detail, it becomes clear that creation is not occurring within the machine. First of all, computer graphics are not unique. Computers have yet to generate anything that cannot be done by hand—and usually already has been done. Second, the apparent ability of the computer to “act on its own” is the outcome of thousands of hours of patient human effort to refine its instructions. The computer can manipulate a shape for us if we have already informed it what a shape is, what the rules for shape manipulation are, what this specific shape is, and so forth.
You can start an automobile engine and it will run by itself, too, but that doesn’t mean it’s being creative. It’s just running.
Somebody in 1975 had a better understanding of why artificial intelligence is not in any way “intelligence” than the majority of today’s intellectual minds.
EXCUSE ME THERE IS A PLANT THAT CAN MIMIC FAKE PLANTS?????
IT'S CALLED A BOQUILA TRIOFOLIOLATA AND IT'S FUCKING WITH MY BRAIN
IT APPARENTLY CAN MIMIC OTHER PLANTS AND AT FIRST I WAS LIKE "oh cool man it must take it's genetic code and copy it or feel the roots or something like that!! :3"
AND THEN I READ AN ARTICLE ON IT AND THESE FUCKING PARAGRAPHS HIT ME LIKE A BUS
LIKE READ THIS SHIT
WHAT THE FUCK MOTHER NATURE
I went to find the article. It's fascinating.
In retrospect, consider the number 1 thing every grade-schooler knows about plants is they take in light, the idea they might be able to see should not wreck my shit as hard as it does
How to say the weather is crazy hot in Italian:
Fa caldo! =It's hot Si muore di/dal caldo! =One might day from how hot it is Fa un caldo pazzesco!= It's crazy hot Fa un caldo fotonico*!= It's crazy hot (*slang; photonic) Fa un caldo allucinante!= It's crazy hot (hallucinating) Fa davvero caldo!= It's very hot Fa troppo caldo! =It's too hot Fa un caldo che non si respira! =It's so hot you cannot breathe Fa caldissimo! =It's too hot Ma che caldo fa?! =How hot is today?!
(fa) Un caldo, ma un caldo....(che non se ne può più)!!!=(It's) hot, so hot...(I cannot afford it anymore) we may leave a lot implied in such a sentence. We oftenuse it at the past, like when we're tlaling about an experience we had, anholiday somewhere, and someone asks us how was the weather like. We can answer "(Faceva) un caldo, ma un caldo....!" stressing with our body as well, how hot it was (moving our hand from left to right), bending ligthly and closing our eyes/forrow a little our forehead. Even the memory of that heat causes us pain LOL.
And if you're sweating A LOT: Sono tutt* appiccicat*/appiccicaticci*=I'm (all) sticky/muggy Sono sudatissim* =I'm dang sweaty Mi sto sciogliendo! =I'm melting myself I cannot think about other sentences to add now cause yeah, my brain is sweating too, but I will add some more as soon as I remember.
Fa un caldo fotonico
non l'ho mai sentito, da quale regione proviene? 👀
A dire il vero fotonico, ginormico... credevo fossero aggettivi che si usano un po' dappertutto come sinonimi di "infinitamente grande, più che enorme, (più che) allucinante" quindi boh? Non so se sia slang giovanile, online o entrambe Comunque qui è il Piemonte che parla