Open Badges for Promoting Open Practices in the Institutional Repository: A Pilot Project
Bibliographic record
Abstract
INTRODUCTION This paper describes a pilot project conducted at a mid-sized research university to integrate an Open Badge into the institutional repository (IR) alongside research articles. The Open Badge was intended to indicate that the research article in question complies with a national funders’ open access (OA) policy. METHODS This study employed a two-step process to investigate the value of badges: first, researchers were surveyed to ask their opinions about using badges in the IR; second, user testing was done with a small group of researchers to assess whether badges are easy to apply during the process of depositing an article to the IR. RESULTS A minority of respondents to the survey indicated that they saw value in an open badge. Participants in the testing component revealed several areas where the overall interface to the IR submission process could be improved. DISCUSSION It was clear that there are opportunities to promote open practices relating to national funders’ open access policy in our sample. However, any incentive represented by an open badge may be overshadowed if the infrastructure in which it is presented is not sufficiently streamlined. CONCLUSION Scholars are not willing to spend much, if any, additional time to indicate compliance with an open access policy. Adding an open badge was neither an incentive nor a disincentive for promoting open practices.
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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 | 0.087 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".