Bibliographic record
Abstract
Evaluation, bones of contention, Injury Prevention Online, how to annoy an editor, and board changes For many, “evaluation” is a feared word. It may be as intimidating for researchers as it is for those responsible for programs. The threat it conveys reflects the difficulty in doing scientifically respectable evaluation studies, and for program people, the ever-present possibility that the result will fail to justify their efforts. In spite of these barriers, we cannot responsibly ignore the pressure to evaluate. We cannot justify applying a different standard to the preventive interventions we advocate than those that apply to pharmaceutical manufacturers, for example. What is sauce for the goose is sauce for the gander: we are all bound by the need to make our programs evidence based. Thus, every preventive initiative should be evaluated as well as resources permit and any that are being promoted that make no attempt to do so must be viewed with caution and skepticism. By far the most challenging tasks for evaluators is assessing the worth of community programmes. In this issue we present two examples of how difficult this can be (p 18 and p 23). As well, invited commentaries (p 6 and p 8) offer words of advice and some of consolation. Their suggestions are important for readers who intend to conduct this sort of evaluation in the future. One of the most daunting issues is that most evaluations compare only one or, at best, two communities, usually before and after an intervention. But no matter how many subjects (or injured people) there may be in each community (that is, no matter how large its population) the main comparison uses an “n”of 2. This is so because from a statistical viewpoint, those in one community share many characteristics, that may affect how they respond to …
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| grok | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| opus | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.420 | 0.714 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.014 | 0.055 |
| Scholarly communication | 0.046 | 0.048 |
| Open science | 0.013 | 0.020 |
| Research integrity | 0.065 | 0.050 |
| Insufficient payload (model declined to judge) | 0.044 | 0.012 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".