Bibliographic record
Abstract
The special report by Clark and colleagues in this is-sue of CMAJ concludes that Canada’s Members ofParliament, who set government funding priorities and vote annually to determine the budget of the Canadian Institutes of Health Research (CIHR) for funding health re-search, have little knowledge of health research and fund-ing.1 Although the authors point out a number of limita-tions to their study, there is little doubt that this is an important start to what should become a call to arms. We must regard this survey of Members of Parliament as a chal-lenge to all of us involved in health research to do a far bet-ter job of informing politicians and policy-makers about the work we do and inviting them to participate in our suc-cesses. We may give ourselves high marks when it comes to teaching the next generation of health care professionals and researchers, but we are failing when it comes to educat-ing key decision-makers.
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 | 0.014 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.066 | 0.053 |
| Insufficient payload (model declined to judge) | 0.041 | 0.023 |
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".