Factors influencing Canadian HASS researchers’ open access publishing practices
Bibliographic record
Abstract
Despite increasing awareness and support for open access (OA) publishing, and the advantages of doing so, there is still a low uptake of OA in some disciplines. We surveyed 228 early and mid-career researchers from 15 public universities in Canada. The Social Exchange Theory provided a theoretical foundation that informed factors investigated in this study. Correlation and regression analyses were used to test research hypotheses, while one-way analysis of variance (ANOVA) was employed to test level of effect sizes within subjects. Findings show that altruism (r =.352, β = .331) influenced researchers’ OA publishing practices whereas visibility and prestige do not, even though they are positively correlated. Furthermore, ANOVA results showed that researchers’ career stages have significant effect on their OA publishing practices as mid-career researchers published more in OA outlets. Therefore, building structures and policies that spur researchers’ altruism towards publishing OA should be a continuous and future approach to achieving the ideals of OA in Canada.
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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 | MetaresearchOpen science Domain: Reporting · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | medium |
| gpt | Open scienceScholarly communication Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".