Research Assessment Reform, Non-Traditional Research Outputs, and Digital Repositories: An Analysis of the Declaration on Research Assessment (DORA) Signatories in the United Kingdom
Bibliographic record
Abstract
Objective – The goal of this study was to better understand to what extent digital repositories at academic libraries are active in promoting the collection of non-traditional research outputs. To achieve this goal, the researcher examined the digital repositories of universities in the United Kingdom who are signatories of the Declaration on Research Assessment (DORA), which recommends broadening the range of research outputs included in assessment exercises. Methods – The researcher developed a list of 77 universities in the UK who are signatories to DORA and have institutional repositories. Using this list, the researcher consulted the public websites of these institutions using a structured protocol and collected data to 1) characterize the types of outputs collected by research repositories at DORA-signatory institutions and their ability to provide measures of potential impact, and 2) assess whether university library websites promote repositories as a venue for hosting non-traditional research outputs. Finally, the researcher surveyed repository managers to understand the nature of their involvement with supporting the aims of DORA on their campuses. Results – The analysis found that almost all (96%) of the 77 repositories reviewed contained a variety of non-traditional research outputs, although the proportion of these outputs was small compared to traditional outputs. Of these 77 repositories, 82% featured usage metrics of some kind. Most (67%) of the same repositories, however, were not minting persistent identifiers for items. Of the universities in this sample, 53% also maintained a standalone data repository. Of these data repositories, 90% featured persistent identifiers, and all of them featured metrics of some kind. In a review of university library websites promoting the use of repositories, 47% of websites mentioned non-traditional research outputs. In response to survey questions, repository managers reported that the library and the unit responsible for the repository were involved in implementing DORA, and managers perceived it to be influential on their campus. Conclusion – Repositories in this sample are relatively well positioned to support the collection and promotion of non-traditional research outputs. However, despite this positioning, and repository managers’ belief that realizing the goals of DORA is important, most libraries in this sample do not appear to be actively collecting non-traditional outputs, although they are active in other areas to promote research assessment reform.
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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 | 0.137 | 0.361 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.003 |
| 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".