Measuring and Comparing Aggregation Inconsistency for Chinese Titles in Two Library Catalogues
Bibliographic record
Abstract
When recording titles in vernacular Chinese characters or in their Romanized form, either a monosyllabic pattern or a polysyllabic pattern can be followed. Previous research has shown that polysyllabic transcription helps reduce ambiguity and tends to increase precision in retrieval. As there are no clear cut rules as to how syllables should be aggregated into lexical units, polysyllabic entries are a potential source of inconsistency in a bibliographic database. The aim of this study is to investigate the inconsistencies in the aggregation of Chinese characters (i.e., syllables) into lexical words in the bibliographic records of two library catalogues. Over 5,000 records from the East Asian Library at Université de Montréal (CETASE) and 5,000 records from the Library of Congress (LC) were analysed and tested for aggregation consistency. Detailed analysis reveals fairly high consistency levels in both sets.Lors de l’enregistrement des titres en caractères chinois vernaculaires ou sous leur forme romanisée, un modèle monosyllabique ou polysyllabique peut être utilisé. Des recherches antérieures ont démontré que la transcription en polysyllabes atténue les ambiguïtés et tend à améliorer la précision lors du repérage. Puisqu’il n’existe aucune règle fermement établie sur la manière avec laquelle les syllabes doivent être agrégées en unités lexicales, la transcription polysyllabique est une source potentielle d’inconsistance dans les bases de données bibliographiques. Le but de cette étude est d’examiner l’inconsistance dans l’agrégation des caractères chinois (c’est-à-dire des syllabes) des mots lexicaux contenus dans les notices bibliographiques de deux catalogues de bibliothèques. Plus de 5 000 notices du Centre d’études de l’Asie de l’Est de l’Université de Montréal (CETASE) et 5 000 notices de la Library of Congress (LC) ont été analysées et la consistance de l’agrégation a été vérifiée. Une analyse détaillée révèle des niveaux de consistance élevés pour les deux ensembles.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".