Artificial Intelligence in Subject-Specific Library Work
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it