Integrating Knowledge from Different Sources for Automatic Back-of-the-book Indexing
Bibliographic record
Abstract
The paper reports research on automatic back-of-the-book indexing. It presents a methodology which brings together knowledge from different disciplines. It is inspired by human indexing methodology and the results are more similar to manually-crafted indexes than those produced by previous automatic approaches. Issues of evaluation and applications are addressed.Cette communication présente les résultats de recherche sur l'indexation automatique de livres. L'étude propose une méthodologie qui rassemble des sources de connaissances provenant de disciplines différentes. La méthodologie s'inspire de l'indexation humaine et les résultats se rapprochent plus de l'indexation manuelle que les autres méthodes d'indexation automatique. Sont également touchés les enjeux d'évaluation et d'applicabilité.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".