Diachronic and Synchronic Indexing: Modeling Conceptual Change in Indexing Languages
Bibliographic record
Abstract
This paper outlines a model of conceptual change in indexing languages. Findings from this modeling effort point to three ways meaning and relationships are established and then change in an indexing language. These ways: structural, terminological, and textual point to ways indexing language metadata can aid in managing conceptual change in indexing languages.Cette communication esquisse un modèle du changement conceptuel des langages d’indexation. Les résultats de cette tentative de modélisation convergent vers une triple dimension. Les relations sont établies, puis modifiées dans un langage d’indexation. Ces dimensions, structurelle, terminologique et textuelle, indiquent de quelle manière les langages de métadonnées peuvent contribuer à la gestion du changement conceptuel des langages d’indexation.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".