Bibliographic record
Abstract
The Open Research Area (ORA) for Social Sciences is an international initiative that provides social science research funding and support. It was founded in 2010 by members of the Bonn Group and based on agreement by European social science funding bodies The Agence Nationale de la Recherche (ANR), France, the Deutsche Forschungsgemeinschaft (DFG), Germany, the Economic and Social Research Council (ESRC), UK, and the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands. The Social Sciences and Humanities Research Council (SSHRC), Canada, later joined, as well as the Japan Society for the Promotion of Science (JSPS) as an associate member. ORA facilitates collaborative social sciences research by bringing together researchers from participating countries. Researchers from the partner countries who fulfil the eligibility criteria of their national funding organisation apply to the ORA office handling the year's applications and Japanese researchers submit their applications to JSPS Tokyo. ORA accepts applications from all areas of the social sciences and there is a key focus on supporting young researchers at the beginning of their careers, helping them to extend the reach of their work and network on an international scale. Ultimately, ORA exists to drive forward high-quality research and strengthen international collaboration in social sciences research. So far, five rounds of ORA have been successfully completed, with more than 60 international collaborative proposals funded across diverse social sciences fields, including political science, economics, empirical social science, psychology, geography, urban planning and education science.
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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 | 0.094 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.004 | 0.040 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.078 | 0.019 |
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".