Bibliography of Canadian Educational History/Bibliographie d’histoire de l’éducation canadienne
Bibliographic record
Abstract
This issue continues the bibliography on Canadian educational history and related fields most recently listed in Volume 22, number 2 (Fall 2010).Please note that suggestions for possible inclusion in the bibliography of Canadian educational history are welcome and should be forwarded to the Historical Studies in Education editorial team.Nous poursuivons ici notre bibliographie de l'histoire de l'éducation au Canada et autres domaines connexes, bibliographie dont la dernière mise à jour fut effectuée dans le volume 22, numéro 2 (automne 2010).S'il-vous-plaît, noter que les suggestions pour une éventuelle inclusion dans la bibliographie de l'éducation canadienne sont les bienvenues et doivent être envoyées à l'équipe éditoriale de la Revue d'histoire de l'éducation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.039 | 0.088 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.010 |
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".