Bibliographic Induction: How KO Systems Optimize Browsing by Supporting Library Users' Prior Knowledge
Bibliographic record
Abstract
We investigate category-based induction as an aspect of browsing a library collection. Category-based induction is one of the primary uses of categories that are stored in memory. Knowledge organizing systems represent concepts in broadly the same way as models of category-based induction. Accordingly, it is reasonable to suppose that knowledge organizing systems facilitate category-based inductions about the collections that they organize. The processes of familiarization and differentiation are key aspects of browsing (Ellis 1989). Intuitively, these approaches appear to involve category-based induction in a bibliographic context. By examining induction, we hope to shed new light on the role of knowledge organizing systems in shaping browsing behavior. We also seek to investigate the viability of using inductive confidence as a dependent variable in assessing the utility of a KOS. A system that supports induction is potentially of great benefit to people seeking to browse a collection, whether the collection exists virtually or is part of a library’s physical stacks.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".