Bibliographic record
Abstract
[i walk the judy garland trail] Lucas de Lima (bio) i walk the judy garland trail& pause when i feel spirits congeal;a douche of the soul, the emanationof those still here like half-eaten deer.the mother duck halfway to the pines, her broken leg.when i sing on the shoreline, a call & responsemy areolas contract starlike.the dunes rolling, the waves crashingremind me i’m not alonebut i’m also alone.a cloud over me like skin, trying to encase this worldin a viscous membrane. [End Page 185] Lucas de Lima lucas de lima is a Brazilian-born poet, artist, educator, scholar, and translator. They are the author of Wet Land (Action Books) and Tropical Sacrifice (Birds llc). Their writing has appeared in pen Poetry, Poetry Foundation, boundary2, Apogee, Syndicate, and Brooklyn Rail. They hold a PhD from the University of Pennsylvania and are the recipient of grants and fellowships from the Charlotte W. Newcombe Foundation, Canada Council for the Arts, and the Social Sciences and Humanities Research Council of Canada. De Lima teaches at Mount Holyoke College. Copyright © 2023 Lucas de Lima
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.029 |
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".