The Open Scholarship Commons: Advancing Research for the Public Good
Bibliographic record
Abstract
The ground is shifting at our educational institutions. We are asked to demonstrate the value of our research to a wider public, particularly as public funding comes with requirements of openly sharing funded research results. At the same time, we are witnessing an educational shift from students as consumers of knowledge to students as co-creators of new knowledge. How can libraries, the traditional keepers and guardians of knowledge, grow to support this changing world? Our library will offer a new space that brings together partners at our academic institution to support the entire research cycle from the conception of an idea to the creation and dissemination of new knowledge in service of advancing research for the public good. Through this paper, we will share our process for creating this new space and how it will open opportunities for the production and sharing of scholarship beyond the academy.
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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 | 0.044 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.049 | 0.035 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.022 | 0.019 |
| Insufficient payload (model declined to judge) | 0.062 | 0.030 |
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".