Bibliographic record
Abstract
This Commentary addresses the ongoing disagreements between many safety advocates who endorse traditional models of prevention and those who oppose them, arguing that safety measures are offset by risk compensation (RCT). The debate is especially heated with respect to regulatory or legislative prevention measures. After explaining the rationale behind risk compensation (aka risk homeostasis theory) (RHT), I provide examples of RCT studies to explain why I believe they should be rejected. The main basis for my rebuttal, however, rests on data that show steady declines in unintentional injury mortality, which, according to RCT, should not have occurred. There are many other reasons for rejecting this theory, and it seems that the time has come for the debate to finally be concluded.
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 | 0.072 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.014 | 0.009 |
| Research integrity | 0.066 | 0.076 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".