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
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.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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