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 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 | 0.001 | 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) | 0.001 | 0.003 |
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".