Bibliographic record
Abstract
63 YASMINA DIN MADDEN Snow • Though they washed her with wine And rubbed her with butter it was to no avail. She lay as still as a gold piece. —Anne Sexton, “Snow White and the Seven Dwarfs” hough we washed her with wine and rubbed her with butter and garnished her with all the trappings of success, she would not comply. We gave her everything: the white pony on her sixth birthday; the diamond studs on her twelfth; the apple-red convertible for her sixteenth ; Grandmother’s pearl choker, like so many rows of teeth, for her twenty-first; and then, at twenty-five, she still would not settle down. First, we said it was a stage, a small rebellion before she acquiesced, but that was years ago and still she insisted it was her choice to marry or not, as if she were some kind of bohemian. The first man she brought home was the one with the bun, that greasy topknot of hair sitting up there all through Thanksgiving dinner. Next came the one with holes in his ears so large they’d fit the saucers from the blue Italian bone china tea set we’d gifted her when she was younger. Number three still gives us shivers when we think of the trails of tattoos up and down his arms—snakes, ivy, skulls, and blood drops covering his shoulders and back, creeping around his sides to blanket his chest. When he took off his shirt by the pool, we all gasped in unison. It was her younger brother who yelled, Rad tats!, and who shook with glee whenever she brought a new man home to visit. We all knew the little brother was a lost cause. It was her we’d pinned our hopes on. The fourth one seemed normal at first, but a few minutes into dinner he shared that he was raised in Florida and we all choked on our soup just a little. The fifth man came in a midi-skirt that matched hers, his calf t 64 muscles bulging obscenely below its hem. When he discussed feminist theory at dinner, Grandmother fell asleep before the first course was cleared. The sixth was Canadian, and we have nothing more to say about that. The seventh came to us with flowers and chocolates, but the flowers were carnations and the chocolate was chalky, and when he spoke of the Iowan cornfields behind his family’s farm, we imagined the stink of pigs and wrinkled our noses. Then she came to us alone, a small tattoo of a crown visible above her ankle, a twinkling silver hoop through her nostril, gripping copies of A Vindication of the Rights of Women and Bad Feminist. We took in the cutoff shorts and flannel shirt. She looked like a deranged but very beautiful farmer. We tried to listen to what she was saying, we did, but all we could do was stare at the movement of her rose-red lips, her skin like a fine layer of snow, that waist and those ankles as narrow and delicate as a bird’s ribcage, her glossy black hair that swung loose as she talked and talked and talked and talked. What she said we could not tell you. ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.041 | 0.078 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".