Problèmes de repérage des ressources bibliographiques en langue chinoise : une perspective occidentale
Bibliographic record
Abstract
Ce travail a pour objet de présenter plusieurs avenues de recherche pour le développement de modules de repérage de notices bibliographiques en langue chinoise. Des études antérieures ont montré les succès et les limites du repérage se fondant sur des données chinoises romanisées (pinyin). Il semble qu’une proportion non négligeable des utilisateurs n’obtient pas des résultats très satisfaisants lors du repérage en pinyin. Pour fournir à ces utilisateurs des moyens de repérage mieux adaptés, il est essentiel d’explorer d’autres avenues méthodologiques susceptibles d’être intégrées aux bases bibliographiques dans le contexte nord-américain, où les ressources en langue chinoise représentent habituellement seulement une proportion minime des collections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".