Intervention-Research as a Social-Ecological Transition Belt, Steering Wheel, and Engine
Bibliographic record
Abstract
Abstract Social-ecological transition refers to a social movement and a field of research. As a research field, social-ecological transition proposes tools for steering and inciting large-scale transformations toward sustainability. These transformations must take root in, and impact, the social-technical regimes (healthcare infrastructure, transportation system, food regime) that structure our everyday life. Academic research in itself can be seen as a social-technical regime in which changes may occur, so that science contributes to addressing current challenges. This chapter aims to describe and justify a specific research approach rooted in the spirit of transition: intervention-research, an innovative, transdisciplinary, and praxeological approach. Similar to action research, intervention-research implies active and prior involvement of research teams in transition initiatives, with the explicit intention of social transformation. This approach entails the diversification of roles for researchers toward facilitation, management, and mobilization. The proposed chapter examines three intervention-research projects conducted by LAGORA (Laboratoire de Gouvernances Alternatives) in the Saguenay-Lac-Saint-Jean region, Quebec. It illustrates the various stages of intervention-research, the new roles of research actors, as well as the challenges and limitations of such an approach.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".