DC metadata is alive and well (and has influenced a new standard for education)
Bibliographic record
Abstract
This paper describes the emerging ISO/IEC 19788 Metadata for Learning Resources standard, where DCMI specifications have been adopted into an ISO/IEC standard. The new standard is for development of other standards. It has already been adopted as a normative standard for European education, in Australia and Canada. The new Metadata for Learning Resources standard is a multi-part standard and, at the time of writing, is still being extended. While it commenced as work based on the Learning Object Metadata standard in common use world-wide, it evolved into a Resource Description Framework standard in order to maximize its potential for interoperability. The Dublin Core Metadata Initiative [DCMI]1, as an open community, has collaboratively developed ‘standards ’ for twenty years. The deliberately open nature of DCMI work has meant that people with no known connection to DCMI can nevertheless take advantage of the DCMI work and further develop it. This paper asserts that ‘DC Metadata ’ has provided a solid base for the MLR and shows, yet again, that DCMI work is thriving in the distributed, global environment.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".