Des outils pour mener une recherche-action participative : Le cas des espaces naturels de loisir informels (ENLI)
Bibliographic record
Abstract
This study, conducted in Trois-Rivières since 2020, focuses on participatory action research (PAR) and natural informal recreation areas (NIRA) in Quebec. NIRA characterized by spontaneous vegetation in urban areas, are popular recreational spots for their proximity and social/ecological benefits. The research involves both « engaged citizen » researchers and « citizen co-researchers » using a co-creation approach inspired by living labs. Data collection tools include literature reviews, online surveys, artistic co-creation workshops, citizen walks, and thematic workshops. The methodology aims to establish a new research niche on NIRA and integrate them into local communities. The conclusion highlights the methodical nature of the research, transparency in the positions of engaged researchers and citizens, while acknowledging challenges in transferring results to other territories.
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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 | 0.056 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.024 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".