Investigating the participation of business librarians in academic program reviews using corpus-based methods
Bibliographic record
Abstract
Prior research into the role of business librarians in academic program reviews has relied on surveys and interviews, revealing that librarians perceive that they are marginalized in the review process. Using a collection of program review documentation produced for the reviews of nine graduate programs offered at a Canadian business school, this study employs corpus-based techniques to obtain direct measures of librarian involvement. The findings provide objective confirmation that business librarians are not well integrated into program reviews overall, and that their contribution to the reviews of professional programs is even more limited than their contribution to the reviews of research-oriented programs. Based on best practices and missed opportunities observed as part of this study, seven strategies are suggested for integrating business librarians more fully in the program review process for the benefit of all program stakeholders.
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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 | 0.109 | 0.388 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".