Just-in-Case or Just-in-Time Library Services: The Option and Usage Valuation of Libraries and Information Services
Bibliographic record
Abstract
The efficiency of libraries and information services has been legitimately put to test. However, most evaluations of libraries and information services have measured usage value only and exclude option value. Measuring both values might clear existing doubts on the value and efficiency of libraries and information services. The paper argues that many previous evaluations of libraries and information services used ineffective methods and approaches, evaluates some of these methods, and suggests a model that might overcome previous approaches’ shortcomings.L’efficacité des bibliothèques et des services d’information a été légitimement mise à l’essai. Cependant, la plupart des évaluations des bibliothèques et des services d’information ont mesuré la valeur de l’utilisation exclusivement et ont exclu la valeur du choix. La mesure de ces deux valeurs pourrait éclaircir les doutes existants sur la valeur et l’efficacité des bibliothèques et des services d’information. Cette étude soutient que plusieurs évaluations antérieures des bibliothèques et services d’information ont utilisé des méthodes et des approches inefficaces, évalue quelques-unes de ces méthodes et suggère un modèle qui pourrait combler les lacunes de ces précédentes approches.
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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 | 0.023 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".