Bibliographic record
Abstract
The purpose of this study was to examine mock jurors’ decisions when faced with a case involving a bully charged with the suicide death of a victim. Mock jurors read a fictional trial transcript detailing the final months of the victim’s life, in which the 16-year-old victim was repeatedly bullied by the 18-year-old defendant. Manipulations included: sex of the victim and defendant (i.e. both were female or both were male), nature of the bullying (i.e. directly threatening to kill the victim or indirectly telling the victim to kill himself), and the medium used by the bully (i.e. no bullying occurred online or some bullying occurred with the use of the internet). Most mock jurors were in favor of convicting the defendant, particularly when both parties were male and the defendant was accused of repeatedly telling the victim to kill himself both on- and offline. Over 80 percent of the mock jurors stated that they would like to see bullies incur criminal charges. Approximately 50 percent indicated that they felt a charge as severe as manslaughter was warranted in cases where the victim commits suicide.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".