Collaborative Research, Public Inquiry, and Democratic Experimentalism: Contributions and How to Apply Pragmatism to Social Innovation Studies
Bibliographic record
Abstract
This article explores the contributions of a pragmatist approach to social innovation studies. It characterizes the epistemological assumptions of pragmatism and its implications to conceive of “science in action.” It explores the contributions of pragmatisms in developing a perspective to analyze civil society and its action to promote social innovation, focusing on the key notions of “public inquiry” and “democratic experimentalism.” The aim is to discuss the contributions, challenges, and limits of conducting pragmatic studies—from an analytical and methodological perspective—giving way to co-operative and engaged research that connects and co-ordinates teaching and knowledge transfer, theory and practice, experts and ordinary citizens, and knowledge and experiences in social innovation studies.
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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 | 0.140 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.221 |
| Scholarly communication | 0.023 | 0.043 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".