Researcher Perspectives on Obstacles and Facilitators of Open Scholarship at a Canadian University
Bibliographic record
Abstract
In 2022, researchers at Dalhousie University were surveyed to assess their understanding and practice of open scholarship. The survey was designed to answer these primary questions: what are Dalhousie University researchers' existing practices and levels of knowledge regarding open scholarship, and what is their awareness and perception of institutional support for open practices? Participants were recruited through direct email, blog posts, and newsletters from the Dalhousie Libraries, Faculty of Graduate Studies, Office of Research Services, and offices of the Associate Deans of Research. During the three-week period the survey was active,131 surveys were begun. As incomplete surveys were excluded from data analysis, the total analyzed sample size was 98. Descriptive analysis was conducted, as the number of responses was not representative of the Dalhousie University population. Most responses were from faculty, specifically in the Faculty of Medicine, followed by the Faculties of Science and Health. The majority of respondents reported sharing some type of scholarly output, though this varied by discipline and by material type. Informal sharing mechanisms were reported more frequently than formal repositories or publisher sites. Obstacles to open scholarship practices that were identified included concerns about investments of time, money, and education as well as concerns about institutional support and recognition. While many supports for open scholarship are available, there is a need to increase awareness.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".