Measuring the Performance of the Cochrane Library
Bibliographic record
Abstract
How well does The Cochrane Library achieve its objectiveto provide accessible and credible evidence to guide decision making in medicine and public health?And how should we measure success or failure?Regular users of The Cochrane Library will have a view of its quality, and we hope that you think that it's good and improving.One of our tasks as the Cochrane Library Oversight Committee (CLOC) is to report to the Steering Group of The Cochrane Collaboration on the performance of the Library and its Editor in Chief.[1] In general we are satisfied with both.But we thought it important to try to introduce some objectivity into the evaluation of The Cochrane Library by devising a set of metrics.Together with the Cochrane Editorial Unit we have done so, and they are shown in Table 1 along with their values for the past three years.We welcome your feedback on the metrics we have devised.To be useful, metrics must provide information on progress in relation to the aims of the Library.In addition, it must be possible to measure them precisely and relatively easily.They must also change over time at a speed that is useful-not as fast as hourly but not as slowly as five yearly. Measuring the performance of The Cochrane Library (Editorial) 1
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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 | 0.176 | 0.636 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.057 | 0.039 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 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".