Bibliographic record
Abstract
Epidemiology occupies a unique role as a knowledge-generating scientific discipline with roots in the knowledge translation of public health practice. As our fund of incompletely-translated knowledge expands and as budgets for health research contract, epidemiology must rediscover and adapt its historical skill set in knowledge translation. The existing incentive structures of academic epidemiology - designed largely for knowledge generation - are ill-equipped to train and develop epidemiologists as knowledge translators. A useful heuristic is the epidemiologist as Accountable Health Advocate (AHA) who enables society to judge the value of research, develops new methods to translate existing knowledge into improved health, and actively engages with policymakers and society. Changes to incentive structures could include novel funding streams (and review), alternative publication practices, and parallel frameworks for professional advancement and promotion.
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 | 0.068 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.021 | 0.045 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".