Investigating Factors Affecting Artificial Intelligence (AI) Adoption by Libraries at Top-Rated Universities Worldwide
Why this work is in the frame
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Bibliographic record
Abstract
This chapter aims at understanding better the perceptions of academic libraries' top managers with regards to artificial intelligence (AI) and adoption decisions. Building on previous research, this study reports on the relationship between perceived familiarity with AI and the decision-makers' positive adoption attitude regarding AI in libraries. Results indicate that the main issues faced by senior-librarians when considering an AI implementation are the lack of maturity of the commercial solutions offered, security and privacy concerns, the library's core values, its funding capacity, and the need for technical expertise. The most popular AI applications currently implemented are natural language processing and virtual reference librarians (chatbots) while the least popular are robot guides and facial recognition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.737 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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