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 …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".