Institutional repository digital object metadata enhancement and re-architecting
Bibliographic record
Abstract
We present work undertaken at our institutional repository to enhance metadata and re-organize digital objects according to new information architecture, in an effort to minimize administrative object management and processing, and improve object discovery and use. This work was partly motivated by the launch of a new discovery platform at our institution, which aggregates metadata and full text from our four open access repositories into a cohesive, consistent, and enhanced searching and browsing experience. The platform provides digital object identifier (DOI) assignment, metadata access via various formats, and an open metadata and full text application program interface (API) for researchers, amongst other features. Functionality of these platform features relies heavily on accurate object representation and metadata. This work facilitates and improves the discovery and engagement of the diverse digital objects available from our institution, so they can be used and analyzed in new, flexible, and innovative ways by a myriad of communities and disciplines.
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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 | 0.032 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".