D3.5 Assessment Of Implementation Priorities For International Alignment
Bibliographic record
Abstract
An important activity for COAR is to align repository networks in order to create a seamless global repository network and demonstrate that repositories offer a viable solution for open access. The activities target three levels of engagement: (1) strategic, (2) technical and semantic interoperability, and (3) services. In May 2017, eight regional/national initiatives signed an International Accord for Repository Networks (Australasia, Canada, China, Europe, Japan, Latin America, South Africa, and United States). The aim of the accord is to increase collaboration across regional repository networks in order to strengthen and enhance the distributed, community-based open access infrastructure around the world. A distributed system will ensure responsiveness to local needs and contexts and also reduces the risk of commercial buy out. However, in order to support the development of value added services on top of repositories, we need to ensure the widespread, international adoption of common functionalities, open APIs, and standard approaches to vocabularies and metadata. Based on the principles and agreements outlined in the International Accord, the signatories have agreed to the following four next steps: 1. Common global vision for repositories 2. Implementation of next generation repositories (NGR) 3. Adoption and improvement of networked services and technologies 4. Standard vocabularies and metadata This document provides an assessment of progress to date in each of these four areas.
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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 | 0.169 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.036 | 0.021 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 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".