The Edges of Institutional Repositories
Bibliographic record
Abstract
Findings show that while most universities maintain institutional repositories (IR), they are less present in colleges. Additionally, few institutions provide clear, public-facing policies defining who can contribute and what types of content are included. Inconsistent acceptance of student work beyond graduate theses and dissertations, along with inconsistent treatment of creative works like podcasts, zines, and visual art, suggests structural and policy gaps. These gaps shape and influence which contributors and forms of knowledge are recognized and validated as scholarly. For students, this can limit opportunities to develop researcher identity and agentic voice. For all, it highlights how repository practices can reflect and risk reproducing institutional hierarchies of value. Focused attention on the capacity and potential of IRs can strengthen scholarly communications outreach and education. Deliberate policies and practices that actively welcome and incorporate student work and grey literature within IRs can advance equity, open pedagogy, and more inclusive models of knowledge preservation. The decision-making within IRs impacts students, faculty, and the broader information community, with implications across the information sciences, libraries, archives, museums, and knowledge dissemination. This article is about the edges of institutional repositories: what kinds of knowledge are validated, preserved, or marginalized in Canadian IRs, and how this affects students going forward.
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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 | 0.018 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".