"Am I My Brother's Keeper?"1: Reforming Criminal Hazing Laws Based on Assumption of Care
Bibliographic record
Abstract
One hundred years ago, two states had criminal laws addressing collegiate hazing. Today, hazing is a crime in thirty-nine states. However, this flood of legislation has failed to stem the tide of hazing injuries and deaths. The current criminal law approach to hazing has failed because the claimed benefits of specialized hazing laws are illusory. Moreover, the rare cases in which hazing laws provide a benefit over general criminal statutes are the very cases in which the hazing laws are most vulnerable to legal challenge. The current approach also fails on policy grounds. A pure enforcement approach that does not engage with students’ values and beliefs about hazing may have the unintended effect of entrenching pro-hazing norms. The creation of sweeping criminal liability also increases the danger of hazing by driving it further underground.This Comment argues for jettisoning the current, failed approach to hazing and instead imposing a duty of mutual aid on members of collegiate student groups. Under this Comment’s proposal, if a student becomes helpless as a result of a group activity and is unable to protect himself, other group members must protect him from injury until he is once again able to take care of himself. Criminal liability attaches when a member who knows of the other student’ s helplessness breaches the duty and an injury results.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".