Bibliographic record
Abstract
Author's note: This paper is written in the first person for two reasons. First, I teach writers to use the “active voice” whenever possible and must practise what I preach. Second, and more germane, this is a highly personal account of my impressions of an area in injury prevention that I make no pretence I know well. It is an account of a voyage of discovery and cannot be dignified by the trappings of an objective assessment that typical scientific writing conveys . When I was invited to give this wrap-up talk, I was flattered but wondered what someone whose main interest is not occupational injuries might have to say that could be of interest to the audience. In light of this uncertainty, I decided to base this presentation largely on my experiences as editor of this journal in the field. I was invited when our family was preparing to celebrate both Passover and Easter. At the Passover meal it is customary for the youngest to ask four questions, the theme of which is rhetorical along the lines of “Why is this night different from all others in the year?” Then the answers are given each beginning with “On all other nights we do so such and such, but on this night we do thus and such”. Together the questions and answers summarise the Passover ritual. It struck me that this was the sort of question I asked myself after receiving the invitation. I wondered in what way occupational injuries differ from most other injuries such that I knew so little about them. I also wondered what I could say about publishing material pertaining to these injuries that might be different from publishing studies about seat belt use, for example. Consequently, I decided that I would frame this presentation around …
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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 | 0.029 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".