Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Open access is the focus of the evidence summaries in this issue.The benefits of open access are familiar to many librarians.Making publicly funded research openly accessible serves a common good by advancing discovery and innovation, enhancing public welfare, providing an evidence base for sound policy, as well as many other societal benefits (Canadian Association of Research Libraries, 2013).We have a short list of summaries this issue.The first article summarized investigates librarians' perspectives on their role in promoting open access and the barriers and requirements for successful promotion.Next is a summary of an article that explores faculty perspectives on and experiences with open access and sharing of unpublished open content.The final evidence summary looks at an article that showcases techniques for archiving open access articles in an institutional repository.We hope these summaries provide useful to librarians involved in, or simply interested in, open access endeavors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communicationOpen science Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
| gpt | Scholarly communication Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.086 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.095 | 0.782 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it