Bibliographic record
Abstract
FOREWORD I would like first to thank all SIMPOC staff for having opened their door to me with frankness and generosity. It is an important sign of maturity as evaluators are usually seen with suspicion and defensive attitudes are more the rule than the exception. They have been looking to this evaluation as a help for their own self-evaluation. I hope they will not be too disappointed by its candid content and tone. I am, indeed, impressed by the cumulative knowledge they represent in SIMPOC. My main preoccupation is that this knowledge, lessons learned and know-how do not remain confined to individuals but are exchanged and documented within SIMPOC and shared with the outside world, starting right next door with IPEC/OPS. By so doing, SIMPOC’s relevance, efficiency, effectiveness and outreach can continuously improve. If SIMPOC staff experience and lessons learned are not fully documented in an up-to-date and easily accessible system, anyone leaving SIMPOC for whatever reason means an incredible loss of knowledge for the Programme. It also means that new arrivals must reconstruct the many parts of a complex system for themselves. This was in a way my experience in carrying out this evaluation, never entirely sure that I wasn’t missing
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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 | 0.081 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
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".