Bibliographic record
Abstract
Purpose To explain the background, functionality, and content of the CARL metadata harvester and search service, http://carl‐abrc‐oai.lib.sfu.ca/, and to outline plans for improving the service. Design/methodology/approach – This case study employs simple statistical analyses to a set of harvested metadata. Findings This paper documents the use of unqualified Dublin Core (uDC) elements in the metadata harvested from the repositories participating in the CARL harvester, and identifies patterns in the use of that metadata. It also compares these findings with a similar study, and identifies areas for further research. Research limitations/implications This paper is limited to discussion of the characteristics of a relatively small set of metadata collected using the Open Archives Initiative Protocol for Metadata Harvesting. However, analyses reveal some patterns in the use of this metadata that are valuable in the development of best practices for repository implementers. Practical implications This paper documents the use of uDC elements by a specific community. Its findings will form a basis for developing mechanisms for improving the effectiveness of the metadata generated by that community and therefore the services built around that metadata. Originality/value While there are several other studies that take an approach similar to that taken in this paper, no one has yet studied this specific data set. More generally, this paper contributes a valuable case study to research on the implementation of the Open Archives Initiative Protocol for Metadata Harvesting.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".