Towards a more meaningful involvement of librarians in academic program reviews
Bibliographic record
Abstract
Purpose Using a descriptive case study approach, this paper aims to validate academic librarians’ perceptions that they are marginalized by faculty during academic program reviews, and recommends ways for the two groups to collaborate more effectively to make program reviews more meaningful. Design/methodology/approach The paper describes a case study at a Canadian university where the six types of documents produced as part of the program review process for ten graduate programs were analyzed using corpus analysis tools and techniques, such as keyword generation and key word in context analysis. For each program, documents were examined to determine the volume and nature of the discussion involving libraries in the self-study, library report annex, site visit itinerary, external reviewers’ report, academic program’s response and final assessment report. Findings The empirical evidence from the corpus analysis validates the findings of previous perception-based studies and confirms that librarians currently have a minor role in program reviews. Best practices and gaps emerged, prompting five recommendations for ways in which academic librarians can play a more meaningful role in the program review process. Practical implications The results suggest that programs are not currently putting their best foot forward during program reviews, but this could be improved by including librarians more fully in the program review process. Originality/value The present study contributes to the existing body of knowledge about the role of academic librarians in the program review process by providing direct and empirical measures to triangulate previous perception-based investigations that rely on surveys and interviews. It summarizes limitations of the current institutional quality assurance process and the benefits to be gained by involving librarians more in the process. It offers recommendations for policymakers and practitioners with regard to potential best practices for facilitating librarian involvement in academic program reviews.
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.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".