Point & Counterpoint The Purpose of Institutional Repositories: Green OA or Beyond?
Bibliographic record
Abstract
Institutional repositories (IRs) have a conflicted history in terms of purpose. Although always closely associated with the open access movement, in particular open access to the published research through self-archiving (“Green” OA), an approach long championed by Stevan Harnad (e.g., Harnad, 1999) and others, some of the most influential and visionary early essays on IRs speak of them as providing infrastructure for the stewardship of a wide range of institutional output (Lynch, 2003) and as a new way for libraries to support publishing functions (Crow, 2002). And while many libraries have concentrated on green OA to fill their IRs—with or without mandates, always with mixed success— many more have slowly but surely built successful, thriving IRs by providing stewardship of and access to the grey literature, the theses and dissertations, the undergraduate research, and the research data produced on their campuses. In fact, we would argue that libraries are better placed to implement green OA resolutions and mandates when their IR is already well populated and well used with other critical institutional content. An IR should focus on the “I”—on the output of the institution, created by individual researchers producing much more than published peer-reviewed articles.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.035 | 0.051 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".