Currents in Environmental Education: Mapping a Complex and Evolving Pedagogical Field.
Bibliographic record
Abstract
The purpose of this article is to bring to light and celebrate the richness of the environmental education field, thereby paying homage to the pedagogical creativity of its architects over the course of the last thirty years, as well as to their contribution in reflecting on the meaning, problems and possibilities of our relationship to the environment, and on the role of education in this regard. A typology of the different practical and theoretical propositions is presented, in the form of 15 currents of intervention, enabling a mapping of the pedagogical landscape of environmental education. This cartography offers points of reference for critical analysis of discourses and practices in this educational field. It spreads out a rich diversity of inspiration sources for enriching programs or curricula and for conceiving appropriate strategies. Resume Cet article a pour but de mettre en lumiere et de celebrer la richesse du champ de l’education relative a l’environnement, rendant ainsi hommage a la creativite pedagogique de ses artisans au fil des trente dernieres annees et a leur effort de reflexion sur le sens de notre rapport a l’environnement et sur le role de l’education a cet effet. Une typologie des differentes propositions theoriques et pratiques, sous forme de 15 courants d’intervention, permet de tracer une carte du territoire pedagogique de l’education relative a l’environnement et d’apprecier l’ampleur et la diversite du domaine. Cette typologie offre des reperes favorisant l’analyse critique des discours et des situations educatives; elle se deploie comme un ensemble de sources d’inspiration pour l’enrichissement des programmes ou curriculums et pour la conception de strategies appropriees. An overview of the literature in the field of environmental education shows that, despite their shared concern for the environment and their recognition of the central role of education in enhancing human-environment relationships, various authors (researchers, professors, educators, facilitators, associations, organizations, etc.) adopt widely differing discourses on environmental education, and propose diverse ways of practicing educative activity in this field. Each advocates his or her own vision—we may even identify different pedagogical “chapels,” all distinct proponents of the right approach, the best program, the appropriate method. How can we orient ourselves amid such a diversity of propositions? How can we discern which voice(s) should inspire our own educational initiatives?
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".