On the Lack of Consensus over the Meaning of Openness: An Empirical Study
Bibliographic record
Abstract
This study set out to explore the views and motivations of those involved in a number of recent and current advocacy efforts (such as open science, computational provenance, and reproducible research) aimed at making science and scientific artifacts accessible to a wider audience. Using a exploratory approach, the study tested whether a consensus exists among advocates of these initiatives about the key concepts, exploring the meanings that scientists attach to the various mechanisms for sharing their work, and the social context in which this takes place. The study used a purposive sampling strategy to target scientists who have been active participants in these advocacy efforts, and an open-ended questionnaire to collect detailed opinions on the topics of reproducibility, credibility, scooping, data sharing, results sharing, and the effectiveness of the peer review process. We found evidence of a lack of agreement on the meaning of key terminology, and a lack of consensus on some of the broader goals of these advocacy efforts. These results can be explained through a closer examination of the divergent goals and approaches adopted by different advocacy efforts. We suggest that the scientific community could benefit from a broader discussion of what it means to make scientific research more accessible and how this might best be achieved.
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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 | 0.341 | 0.557 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.006 | 0.011 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".