Bibliographic record
Abstract
Spermaceti Amanda Hawkins (bio) Keywords Amanda, Hawkins, spermaceti, whale, scrimshaw, land, whaler, oil At first the men thought it a store of foggy white cum.I can forgive this mistake—desire can interrupt logic. The scrimshaw teeth show portraits of women and sirens,jawbones of panoramic landscapes— land and lovers. I want compassion,but I also believe they were as awful as I think—their markall over the oceans and coasts, taking whatever little beauty remained of those beasts.Easy to let myself hate the men in all they desired and all they do, say, take.Did anyone speak out against it? This is the moment I become truly afraidI am hardly doing any different. Often quiet when I need to speak. All logic lost at a woman's touch.I will use every resource I can find to get what I want. I am insatiably curious.What would I choose if I were a man in a time of whaling? What bodies I would reach for and how?What bone would I pick, and would I take a knife to it? [End Page 351] Amanda Hawkins amanda hawkins is a Tin House and Bread Loaf Scholar, a three-time Pushcart nominee, and a recipient of the Editor's Prize for Poetry at The Florida Review. Her work can be found in Boston Review, The Cincinnati Review, Mid-American Review, Orion, Terrain, and Tin House. She holds an MA in theological studies from Regent College in Vancouver, Canada, and is currently an MFA candidate at UC Davis. Copyright © 2021 The Massachusetts Review, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.118 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".