Could action research be socially responsible?
Bibliographic record
Abstract
Partie d'ouvrage - Alors que la notion de «responsabilité sociale de l’entreprise (RSE)» suscite depuis plusieurs années un véritable engouement, elle est beaucoup plus rarement appliquée par les chercheurs et intervenants à leurs propres pratiques de recherche et d’intervention. Recueil de réflexions menées sur le sujet par des chercheurs et praticiens, d’Europe et du Québec, cet ouvrage se subdivise en trois parties complémentaires, articulées autour des principaux débats qui traversent aujourd’hui la recherche-intervention: le difficile équilibre à trouver entre son utilité sociale et le maintien de son indépendance; la problématique du rapport aux acteurs, notamment aux commanditaires de l’intervention; la caractérisation des postures méthodologiques adoptées par les praticiens de la recherche-intervention. S’il s’adresse principalement aux professionnels de l’intervention en organisation – consultants en stratégie, en GRH et en organisation, coaches, formateurs, etc. – cet ouvrage concerne aussi directement les clients de ces derniers – directions des ressources humaines, responsables opérationnels, gestionnaires de projets – ainsi que, de manière plus générale, tout chercheur ou étudiant en sciences humaines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.237 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.112 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".