One Size Fits All? The Portability of Macro-Appraisal by a Comparative Analysis of Canada, South Africa, and New Zealand
Bibliographic record
Abstract
Cet article analyse la méthodologie de la macro-évaluation ailleurs que dans le contexte canadien où cette approche a été développée et examine la pertinence du modèle pour les archives gouvernementales au Canada, en Afrique du Sud et en Nouvelle-Zélande.L'auteur étudie la macro-évaluation d'abord en tant que cadre théorique, puis comme une réponse aux difficultés d'ordre pratique qui découlent des autres approches d'évaluation archivistique.Il se penche ensuite sur les questions de gestion du changement entourant l'adoption de la macro-évaluation dans d'autres juridictions.L'article conclut que tant la théorie que la pratique doivent être prises en compte lorsqu'on envisage l'adoption de nouveaux modèles et que, bien que les conditions locales doivent être considérées, la valeur possible des innovations provenant de l'étranger ne doit pas être ignorée.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".