Bibliographic record
Abstract
Code Grey: Air Contamination Ian Williams (bio) When the only the only game of your days is hunting weed putting the skin to your mouth smoking burning blood it’s only natural that you would need a gatherer. You look through the ascending soul at the flies that fruit your full sink the juice stains on cup rims bread rinds stuck to plates chicken bones and their spilled ink wing skins sucked sauceless and spat out. You usin’ me, dog. Look again squint through the rising grey soul, at your fishing pole by the phone nothing tethered to the line at the gun I know you keep under your rib, not a soul at the end of your bullet. Let me show you how to kill something besides time. You usin’ me, dog. I’m making you useful. [End Page 885] Ian Williams Ian Williams, who received his Ph. D. at the University of Toronto, is an assistant professor of ethnic American literature and culture at Fitchburg State College in Massachusetts. He is co-editor of Misunderstanding Magazine, a Toronto-based literary journal. Copyright © 2008 Charles H. Rowell
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.383 | 0.112 |
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".