Scotland's public libraries are nothing but practical when it comes to deselection
Bibliographic record
Abstract
This paper is based on results from qualitative research into Scotland’s public libraries collection development practices and the thoughts of library staff in regards to deselection (referred to in this paper as weeding). An open-text online survey promoted through professional newsletters, word of month, and social media, solicited rich, personal input from practicing library staff on the role, practice, and future of deselection in public libraries. From 36 responses, three main themes were developed: public perception, the role of governance, and continunity concerns. With pressure to provide the latest technology and published works for users, all in safe, usable spaces, Scotland’s libraries are weeding to remain relevant and responsive. Governance structures controlled policy and implementation of weeding practices, with respondents from Scottish public libraries overwhelming weeding via stock exchanges or with assistance from library headquarter teams/professional staff. The larger concern for collection development staff was having the budget, time and staff to make weeding a continuous, efficient process.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".