"The IR is a Nice Thing But...": Attitudes and Perceptions of the Institutional Repository
Bibliographic record
Abstract
What attitudes and perceptions do faculty members, graduate students, and other stakeholders have regarding the institutional repository (IR)? I conducted a study at the University of Western Ontario through a survey of 316 participants from various faculties and in roles ranging from graduate students to tenured faculty members, followed by interviews with 10 faculty members and 3 librarians to discuss aggregate results from the survey. Results suggest a course of action for librarians who work with IRs, based on participants’ perceptions of barriers to use (branding, data ownership, resistance to open access (OA), alternative avenues for self-archiving) and elements of the IR participants enjoy and find motivating for use (continued access for graduates, dissertations and theses, pre-print literature reviews, satisfying OA mandates). Suggested next steps to promote IR uptake cover a number of different areas: mediated deposit; clarify benefits for faculty members; communication between library and users; opt-in features; tenure and promotion; enforcing OA mandates; and collaboration.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".