<b>UNDISCIPLINED: </b><b>How do research funders define transdisciplinary research? (RoRI Working Paper No. 12)</b>
Bibliographic record
Abstract
The UNDISCIPLINED project focuses on the i<b>mportance of definitions and descriptions of transdisciplinary research (TDR)</b>. It investigates <b>how research funders define TDR</b>, the facets of meaning within these definitions, and what can be learned from the different approaches used, within a range of TDR funding programmes.The working paper comprises a review of selected literature on the intersection of funding and transdisciplinary research, and an analysis of ‘call for proposal’ documents across six funding programmes (seven calls) in three European research funding agencies. This is followed by a more in-depth series of co-produced case studies, giving insights into six TDR funding programmes from their funders’ perspectives. Three of the case studies provide additional context on programmes outlined in the document analysis (from the Austrian Science Fund, Dutch Research Council and Volkswagen Foundation). The other three provide further context and insights into TDR funding processes (from the Swiss National Science Foundation, King Baudouin Foundation and the Social Sciences and Humanities Research Council of Canada).The working paper concludes with a summary of these evidence strands with reference to our three research questions.
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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 | 0.062 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".