Motivations for Web-Based Scholarly Publishing: Do Scientists Recognize Open Availability as an Advantage?
Bibliographic record
Abstract
The open availability of journal articles is expected to encourage scholars to publish in Web-based publishing venues, as it may provide more visible and wider dissemination of their research. Some studies, however, report no evidence of such relative advantages, although an advantage may be conferred by other factors. Despite emerging disputes about the effects of open availability, scholars' perceptions of the phenomenon are not well understood. Do they recognize the advantages of open availability? Or do they consider other factors more important? This study sought to answer these questions by examining reasons why scholars publish in open-access venues and the extent of their motivations. To accomplish this goal, results were tallied from a Web-based survey of 1104 scientists around the world. The data analysis identified eleven relevant motivational factors: six attitudinal factors, two perceived control factors, and one demographic factor. Together, these factors significantly influenced the intention to adopt open-access publishing. Factors related to social influence and perceived topical compatibility appeared to be insignificant. The influence of attitudinal and perceived control factors, however, varied based on tenure status. The biggest difference between tenured and untenured groups was the rank of perceived visible advantages, implying that open availability has different levels of significance depending on tenure status.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".