Bibliographic record
Abstract
North Atlantic Nancy Kang (bio) And who quenched your thirstwith black sweat turned warm ambera colder continent's sugar-tit tantrums?Who beat the cream and bent them overto bear the sick-churned sweetnessfor their clotted cream and beige milk tea?Whose bare backs were baked brown, stripped red,made porkish lean n salty for so, so little?You stand tall, stand down, sit up, crouch,that we could wear each other's shirtsyour skin, my sweat, twisted intoa maple syrup, cane sugar supplication, wrung out, steeped,swallowed with a tart, spunky lemon rindwaxy sheen, fringed with a burnt-mouthbitter feeling, suckling a stone pacifieranchored to the frozen oceanbeing reeled in, tastinggold hooks. [End Page 260] Nancy Kang Nancy Kang is Canada Research Chair in Transnational Feminisms and Gender-Based Violence, Tier II, at the University of Manitoba. She coauthored The Once and Future Muse: The Poetry and Poetics of Rhina P. Espaillat with Silvio Torres-Saillant, and can be reached at prof.nancykang@gmail.com. Copyright © 2019 Nancy Kang
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.582 | 0.282 |
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".