Insights into the practicalities of collaboration, data and code sharing across the globe.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it