Innovation in Public and Academic North American Libraries, in Words and Deeds
Bibliographic record
Abstract
Being innovative is a popular but ambiguous maxim in LIS. To elucidate how institutions use, and what they mean by the concept, we examine white literature and survey website features of 160 libraries across US and Canada. We identify patterns in the language and ethos of modern innovative librarianship.Être novateur est une maxime populaire bien qu’ambigüe en science de l’information. Pour mieux comprendre comment les institutions l’utilisent et quelle est la signification du concept, nous avons analysé les documents officiels et le contenu des sites Web de 160 bibliothèques aux États-Unis et au Canada. Sont identifiés des modèles d’utilisation langagière et l’ethos de la bibliothéconomie moderne novatrice. ***Full paper in the Canadian Journal of Information and Library Science***
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".