Making Up the Difference: Using Custom Reporting to Identify Metadata Inaccuracies in Link Resolver Serial Metadata
Bibliographic record
Abstract
Accurate title and holding level metadata in a library’s link resolver knowledgebase is essential to providing access to ejournal entitlements. As a result of their complex title histories, publisher submitted ejournal metadata often needs to be manually updated in the link resolver knowledgebase to ensure title changes and coverage data align. This article will demonstrate how a custom report used at the University of Alberta Library identifies date discrepancies between link resolver and catalog data to drive link resolver knowledgebase clean up work for priority ejournal collections. It will end by highlighting how quality metadata produced by the library community might be leveraged by vendors to improve ejournal holding data globally in their link resolver knowledgebases.
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.
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 | 0.129 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".