Bibliographic record
Abstract
Schematic Shane Neilson (bio) Look, the girl drew a picture.A blue crayon line rises to the topof the page and off. Anothermeanders, then breaks right,and stops in a big dark scribble-mess. “What is it?” you ask,because she won’t say,even though she usually does. Every word from her mouthis rain. The picture mist-ifies.She rises to dance in a patternthat was the map—you knownow, the picture was a map.Or was it a rain dance step guide?She’s at the messy part, flopping down on the floorto scream; but each kick and beatis rain, rain, rain; the screamis the hardest you’ve ever heardthe rain come down. Is there a schematicfor soothing? Listen, you tell heras she pelts, this is somehowthe strongest sound in the world. [End Page 113] Shane Neilson Shane Neilson is a mad and autistic poet, physician, and critic from New Brunswick, Canada, who has appeared in Poetry Magazine, Literature and Medicine, and Verse Daily. He published The Suspect We with collaborator Roxanna Bennett and Palimpsest Press in the spring of 2023. Copyright © 2023 University of Nebraska Press
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.770 | 0.489 |
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".