Managing the transition to open access publishing: a psychological perspective
Bibliographic record
Abstract
Abstract To manage the transition to the open access (OA) model of scholarly publishing, we need to understand better what enables, encourages and inhibits the adoption of OA publishing among scientists, and to appreciate individual differences within disciplines. The study adopts a psychological perspective to elucidate motivations, capabilities and opportunities for OA publishing among bioscientists in the UK. To identify differences within the discipline, bioscientists with starkly different past practices for disclosing research data and technologies were interviewed. The sampled bioscientists face similar obstacles and enablers in their physical environment, but that their motivations and experience of their social environments differ. One group is strongly motivated by their moral convictions and beliefs in benefits of OA and feels peer pressure related to OA. The other group expresses fewer pro-OA beliefs, holds beliefs demotivating OA publishing, but feels pressure from research funders to adopt it. The former group makes more frequent use of OA publishing, which suggests that only those with strong motivations will work to overcome the social and physical obstacles. The individual differences within the discipline suggest that bioscientists are unlikely to respond to OA policies in the same way and, thus, the appropriateness of one-size-fits-all OA policies is questioned.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".