The Print Book Purging Predicament: Qualitative Techniques for a Balanced Collection
Bibliographic record
Abstract
At previous Charleston Conference meetings, there was much discussion about how to massively and efficiently weed collections across disciplines using quantitative criteria. The presenters recently published an article in Collection Management entitled “Weeding with Wisdom: Tuning Deselection of Print Monographs in Book-Reliant Disciplines” in which they argue for the importance of retaining some print materials in areas such as history and literature where scholars are dependent on older, lesser-used materials for their research and teaching. Presenters offered suggestions and invited discussion on ways to improve the deselection process through the use of qualitative techniques for weeding book-reliant disciplines in an attempt to maximize the quality of a monograph collection.
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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 | 0.232 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".