Project to automate the Canadian Education Index: Looking for a language / Project pour l'automatisation du répertoire Canadien sur l'education: a la recherche d'un langage
Bibliographic record
Abstract
This brief history of the project to automate the Canadian Education Index centres mainly on a consideration of three indexing languages in relation to the objectives of the Canadian Education Index: our own subject headings (L.C. style), ERIC and PRECIS. PRECIS comes closest to meeting our objectives, but as funding has not vet been received, decisions cannot be made. Work continues on the problems of format and cost effectiveness. Pour le projet d’automatisation du Répertoire canadien sur l'Education, dont on trouvera ici une brève historique, trois langages d'indexation furent étudiés dans l'optique des objectifs du Répertoire: nos propres vedettes-matières (style L.C.), ERIC et PRECIS. PRECIS se révéla le plus approprié à nos objectifs mais, au moment de la rédaction de ce rapport, la question du financement n'étant pas encore réglée, les décisions finales demeuraient encore en suspens. Le travail continue sur les questions de format, de coût et d'efficacité.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".