From Uncontrolled Keywords to FAST? Attempting Metadata Reconciliation for a Canadian Research Data Aggregator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How aggregators reconcile repositories’ user-supplied subject keywords is a growing challenge in the metadata profession. While aggregators allow users to search across multiple databases to find information, the search capability is only as good as the supplied metadata. This paper is a case study of a project to reconcile harvested metadata keywords within a research data discovery service. The Federated Research Data Repository (FRDR) Discovery Service is a national, bilingual platform for discovering Canadian research data that harvests metadata from over 90 repositories. This paper outlines the work of a cross-Canada, volunteer group of experts who attempted to develop a semi-automated workflow to map the FRDR subject keywords to Faceted Application of Subject Terminology (FAST) to improve discoverability. The authors, who were members of the working group, discuss why the project failed, the problems encountered, and their thoughts on the future of automated metadata reconciliation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.013 | 0.235 |
| Open science | 0.017 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it