Bibliographic Induction: How KO Systems Optimize Browsing by Supporting Library Users' Prior Knowledge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it