OpenAIRE interoperability metadata guidelines: ensuring a community-driven global governance for the development of the guidelines
Bibliographic record
Abstract
Leveraging on the development of the metadata guidelines and application profiles within OpenAIRE projects over the past twelve years, OpenAIRE A.M.K.E decided in late 2022 to create a working group to continue the progress of these activities, ensuring community convergence and updates. This panel aim to discuss the need for a global synchronization with other regional and national networks beyond Europe, such as Latin-America, Africa, Canada and Japan, where currently the guidelines are being used. Furthermore, OpenAIRE is pointing to ensure a full alignment with the EOSC Interoperability Framework bringing in the discipline perspective, which rises the relevance of these metadata guidelines in the European research area. The theme of Open Repositories 2023, focused on the practices of the international repositories community to develop and implement standards and frameworks for open repositories, drives us to most pertinently propose this panel dedicated to discuss how to ensure a community-driven global governance for the development and implementation of the interoperability guidelines coordinated by OpenAIRE.
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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 | 0.104 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".