Collection development in library and information science at ARL libraries
Bibliographic record
Abstract
Purpose This paper seeks to discuss the results of a 2010 survey of LIS selectors at ARL institutions/libraries that do not support an ALA‐accredited program to learn how and why LIS materials are collected at these institutions. Design/methodology/approach Collection development librarians completed a survey that asked them to describe their institution's selection policies, practices, and budgets for LIS materials, along with their roles as LIS selectors/subject specialists. Findings LIS collections primarily support librarians and staff in their daily work and ongoing professional development. However, most libraries' LIS collections budgets are comparatively small, selectors receive few requests for new materials, and collecting parameters vary by institution, but are limited in terms of subject, publisher, and audience. The majority of LIS selectors are also responsible for collection development in multiple subject areas and most engage in work outside collection development. Originality/value This is the first paper to explore collection development of library and information science materials outside dedicated library school libraries.
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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 | 0.093 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".