Structure, Logic, and Semantics in ad hoc Classification Schemes Applied to Web-Based Libraries in the Field of Education
Bibliographic record
Abstract
This project focuses on a sample of six Web-based libraries in the field of Education. Our analysis explores structural, logic and semantic dimensions, supported by theoretical research in classification and in the area of personal document spaces organization, and by findings of previous analyses of Web directory structures. Our findings expand our understanding of how Web-based resources in education are organized, helping us determine whether categorization schemes and keywords reflect anything else than local perspectives and systems, while bringing together two research traditions issued respectively from knowledge organization and from document and records management.Ce projet est axé sur un échantillon de six bibliothèques sur le Web dans le domaine de l’éducation. Notre analyse explore les dimensions structurelles, logiques et sémantiques, corroborée par la recherche théorique en classification et dans le domaine de l’organisation des espaces documentaires personnels, et par les résultats d’analyses préliminaires de la structure des répertoires Web. Nos résultats développent notre compréhension sur la manière dont les ressources Web en éducation sont organisées, nous aidant ainsi à déterminer…
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".