No time to scale back: gleaning best practices for library involvement in scholarly communication in Canadian research libraries.
Bibliographic record
Abstract
An in-depth understanding of librarian roles in scholarly communication and the organizational structure in which they operate has not been fully investigated within Canadian research institutions. We report the results of semi-structured telephone interviews with 29 librarians from institutions belonging to the Canadian Association of Research Libraries. The study provides recommendations for libraries looking to develop new programs or enrich existing programs.Interviews provided participating librarians with an opportunity to discuss and reflect on their roles in the context of recent developments in scholarly communication and to provide input on how their libraries are committing to this area of librarianship. Participants in both practitioner and administrative roles provided a diversity of opinions related to scholarly communication and its future development in Canada.The study provides recommendations for libraries looking to develop new programs or enrich existing programs in scholarly communication
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How this classification was reachedexpand
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 communication Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | low |
| gpt | Scholarly communication Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: yes | Not applicable | medium |
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 | 0.056 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.032 | 0.015 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".