Bibliographic record
Abstract
The problem of attacks by those who feel threatened by scientific evidence has existed for hundreds of years.1 In the USA, researchers who examine controversial safety-related issues and the institutions that support their studies are well known to have been the targets of threats. Among the topics that have generated storms of conflict in the injury field are firearms laws and requirements that motorcyclists wear helmets.23 It is, perhaps, not so well known that the sources that provide information are also at risk from those who disagree with some of the content. Almost from its inception, SafetyLit4 (a no-cost, World Health Organization-affiliated online resource for current and older injury-related research articles) has received emailed comments from readers who disagreed with part of its content. The early messages simply pointed out possible conflicts of interest by the authors of editorials or policy statements and methodological problems with certain published studies. As the number of visitors to the SafetyLit website increased (>53 000 unique visitors during the first week of December 2007), so did the problem of reader protests. In 2003, the US invasion of Iraq precipitated protests from subscribers in Canada and Eastern Europe. More than 3000 SafetyLit email subscribers expressed their dissatisfaction with the USA and opposition to the war by …
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 | 0.036 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.178 | 0.166 |
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".