Insights into the practicalities of collaboration, data and code sharing across the globe.
Bibliographic record
Abstract
On the occasion of the tenth anniversary of the RDA, and as we approach the end of our Belmont-funded project, PARSEC (www.parsecproject.org), we felt it was time to discuss the practicalities of collaboration, data and code sharing for publication and re-use across the globe. The PARSEC team–from five geographically-dispersed countries–has collaborated for four years on the collation and harmonisation of data and the development of new methods for sharing data and code as we investigate the socio-economic effects of nature conservation initiatives. We have had very profitable partnerships in this endeavour with several leading data infrastructure and research tool providers, including ORCID, DataCite, the RDA itself, and the World Data System. In this session representatives of the key data science infrastructures (ORCID, Scholix, Crossref, DataCite, the WDS, the Environmental Data Initiative) and users (representing the voice of marine conservation, machine learning, data for artificial intelligence, health and life sciences, social inequalities in health, the geoscience community and domain variations in open science and open data) discuss the challenges they face and their vision of the optimum path to the future.
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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 | 0.080 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.033 | 0.037 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".