Needs of Libraries with Rich Islamic Collections: Classification Dilemma and Optimal Solution for Islamic Knowledge
Bibliographic record
Abstract
Standard library classification systems (e.g., DDC, LCC, UDC) are convenient information organization tools. Libraries with rich collections on Islam face problems; hence, use expansions/indigenous systems. This poster examines this problem and presents a potential optimal solution, based on data from 30 libraries and 16 LIS Scholars from nine countries.Les systèmes de classification documentaires standards (p. ex. DDC, LCC, UDC) sont des outils d’organisation de l’information pratiques. Les bibliothèques avec de riches collections sur l’Islam font face à des problèmes et elles doivent utiliser des systèmes complémentaires ou ad hoc. Cette affiche examine ce problème et présente une solution optimale potentielle, basée sur les données de 30 bibliothèques et 16 chercheurs en BSI de neuf pays.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".