Bibliographic record
Abstract
Cet article décrit les liens qui existe entre certains volets de la pédagogie actualisante et une approche d’éducation relative à l’environnement (ERE) basée sur l’apprentissage expérientiel. Nous y définissons tout d’abord ce qu’est l’apprentissage expérientiel pour ensuite décrire ses liens avec l’ERE. Après avoir expliqué de quelle manière l’apprentissage expérientiel facilite l’acquisition ou le renforcement d’attitudes positives envers l’environnement, nous présentons brièvement différents travaux en ERE réalisés en milieu scolaire et communautaire au Nouveau-Brunswick dans une perspective d’apprentissage expérientiel. Finalement, en reliant les objectifs et les composantes de l’apprentissage expérientiel appliqué à l’ERE à ceux de la pédagogie actualisante, nous expliquons comment de tels travaux traduisent dans le concret les principes de cette pédagogie.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.094 | 0.011 |
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; both teacher heads 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".