Metadata Quality in Institutional Repositories May be Improved by Addressing Staffing Issues
Bibliographic record
Abstract
A Review of: Moulaison Sandy, H., & Dykas, F. (2016). High-quality metadata and repository staffing: Perceptions of United States–based OpenDOAR participants. Cataloging & Classification Quarterly, 54(2), 101-116. http://dx.doi.org/10.1080/01639374.2015.1116480 Objective – To investigate the quality of institutional repository metadata, metadata practices, and identify barriers to quality. Design – Survey questionnaire. Setting – The OpenDOAR online registry of worldwide repositories. Subjects – A random sample of 50 from 358 administrators of institutional repositories in the United States of America listed in the OpenDOAR registry. Methods – The authors surveyed a random sample of administrators of American institutional repositories included in the OpenDOAR registry. The survey was distributed electronically. Recipients were asked to forward the email if they felt someone else was better suited to respond. There were questions about the demographics of the repository, the metadata creation environment, metadata quality, standards and practices, and obstacles to quality. Results were analyzed in Excel, and qualitative responses were coded by two researchers together. Main results – There was a 42% (n=21) response rate to the section on metadata quality, a 40% (n=20) response rate to the metadata creation section, and 40% (n=20) to the section on obstacles to quality. The majority of respondents rated their metadata quality as average (65%, n=13) or above average (30%, n=5). No one rated the quality as high or poor, while 10% (n=2) rated the quality as below average. The survey found that the majority of descriptive metadata was created by professional (84%, n=16) or paraprofessional (53%, n=10) library staff. Professional staff were commonly involved in creating administrative metadata, reviewing the metadata, and selecting standards and documentation. Department heads and advisory committees were also involved in standards and documentation selection. The majority of repositories used locally established standards (61%, n=11). When asked about obstacles to metadata quality, the majority identified time and staff hours (85%, n=17) as a barrier, as well as repository software (60%, n=12). When the responses to questions about obstacles to quality were tabulated with the responses to quality rating, time limitations and staff hours came out as the top or joint-top answer, regardless of the quality rating. Finally, the authors present a sample of responses to the question on how metadata could be improved and these offer some solutions to staffing issues, the application of standards, and the repository system in use. Conclusion – The authors conclude that staffing, standards, and systems are all concerns in providing quality metadata. However, they suggest that standards and software issues could be overcome if adequate numbers of qualified staff are in place.
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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 | 0.101 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".