Do world conferences live up to their promise?: Table 1
Bibliographic record
Abstract
Thoughtful feedback is needed This issue of Injury Prevention is scheduled for distribution at the 7th World Conference on Injury Prevention and Safety Promotion in Vienna. Barring the predictably unpredictable quirks of publishing, each delegate will have received a copy. Many can still recall the first conference in Sweden 15 or so years ago, and some will have attended each successive meeting. The total of attendees, past and present, may now be large enough to begin to try to assess how well these biennial pilgrimages meet their goals. There is no way to judge with certainty the success of a conference. Much may depend on the weather (awful in Montreal, delightful in Melbourne) or on which of our old friends showed up. One criterion for success is that held by the organizers: a good balance sheet, which equates to the number of attendees. But bigger is not necessarily better. Balance may be more important—fewer attendees from more countries. For example, this conference may have attracted a higher-than-usual number from some European countries. Viewing the world through the undoubtedly distorted lens of an editor, my impression has been that much of Europe is a desert when it comes to injury prevention. If it proves true that many of the papers given in Vienna originated from those desert lands, this would be one positive score. Still, total attendance may be an appropriate measure even if it is confounded by location, which, in turn, reflects cost considerations. Montreal is easier to reach than Delhi and who could resist Vienna except those with shallow pocketbooks. Apart from numbers and …
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.464 | 0.327 |
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".