Bibliographic record
Abstract
Data set with 127 funding programmes for interdisciplinary research. Table summary (excel) and documents (pdfs). This data set covers funding opportunities to support interdisciplinarity (and other forms of crossdisciplinary research) in Europe and North America. The data has been collected in two rounds. The first round was between November 2018 and February 2019, and the second January–February 2022. Data set covers funding opportunities from both public and private sources, and all scholarly disciplines in both STEM and SSH. The database covered all Pan-European funding and national data sets from Australia, Canada, Denmark, Finland, Ireland, the Netherlands, New Zealand, Nigeria, South Africa, Sweden and the United Kingdom. I collected the guidelines for applicants from each of the funding programmes selected. These were usually in PDF format, and are included in this file. I used the data set to analyse the role of social sciences and humanities in interdisciplinary research. This study will be published in Social Epistemology. This article has more information about data collection and data analysis. If you wish to use this data set for further research, please cite this repository file. If you wish to know more about the study, I am happy to provide details. You can reach me via email, anita.valikangas@gmail.com . Best wishes, Anita Välikangas
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.236 | 0.123 |
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".