Artificial Intelligence in Subject-Specific Library Work
Bibliographic record
Abstract
The general implications of AI for libraries are much discussed in library literature. But while this discussion takes place at the library-wide level, there are also important implications for subject librarians due to the specific uses of AI in different professions and areas of study. These are often overlooked as these specializations tend to publish in subject-specific journals. This article aims to address this research gap by providing a comparison and thematic analysis of this literature. Subject-specific library journals in the areas of law, health sciences, business, and humanities and social sciences were searched to identify relevant journal articles that discussed AI. 131 articles were identified and tagged with at least one category that reflected the nature of the discussion around AI. The following analysis showed that literature related to law had the greatest number of articles by far, though the publishing activity in all disciplines has increased significantly in the last 10 years. This article explores these trends to gain a more comprehensive understanding of the implications for subject-specific library work.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.027 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".