Comment sensibiliser localement à des pratiques écoresponsables ?
Bibliographic record
Abstract
De nombreux administrateurs sont confrontés à la problématique de sensibiliser les individus à des pratiques quotidiennes plus écoresponsables. Mais comment accompagner les usagers afin qu’ils éteignent les lumières et le matériel informatique quand ils quittent le bureau ? Comment favoriser l’usage du covoiturage afin de se rendre sur son lieu de travail ? Nous passerons en revue ici les principales stratégies de sensibilisation appliquées aux comportements écoresponsables. Après avoir présenté les traditionnelles techniques d’information et d’incitation, nous nous intéresserons à des techniques originales reposant sur la réalisation d’un acte préparatoire. Nous verrons que les stratégies comportementales présentent une option pertinente en matière de sensibilisation à l’environnement. Enfin, nous nous intéresserons à la technique de la rétroaction ( feedback ), qui peut être combinée avec les techniques précédemment mentionnées.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".