Bibliographic record
Abstract
“Yo Jaysus, your ass is drippin,” says Max Lechuga. He's the stocky guy in class, you know the one. Fat, to be honest, with his inflatable mouth. “Stand clear of Jaysus' ass, the fire department lost another four men up there last night.” The Gurrie twins huddle around him, geeing him on…. The class detonates through its nose…. Jesus abandons his desk with a crash and runs from the room…. Then Max Lechuga gets out of his chair, and goes to the bank of computer terminals by the window. One by one, he activates the screen-savers. Pictures jump to the screen of Jesus naked, bent over a hospital-type gurney. (Pierre, 2003, pp. 231–233) The courts of law would shit their pants laughing…. But here's why they'd laugh: not because they couldn't see … but because they knew nobody else would buy it. You could stand before twelve good people … and they wouldn't admit it. They'd forget how things really are, and slip into TV-movie mode where everything has to be obvious. (ibid., p. 51) INTRODUCTION Pierre's Vernon God Little illustrates the tortuous power of words and images; the anger, hate, and pain they can promote; and their transformation into depictions and online permanence for viewing by an infinite and global audience, from classroom to cyberspace. The narrative depicts peer classroom bullying and cyber-bullying at its core.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".