Breaking New Ground? Reflections on Greening School Ground as sites of Ecological
Bibliographic record
Abstract
In this paper, we explore greening initiatives in school grounds as sites where ecological, pedagogical, and social transformation might be promoted and take place. Reflecting on our evaluations of school ground greening ini-tiatives in Canada and England, we note that these initiatives are often at the margins of young peoples ’ experiences in schools and that their poten-tial to be truly transformative can go unrealized. A series of tensions are highlighted in addressing a shift towards realizing their potential; these include situating greening school grounds more explicitly within the cur-riculum and securing broader institutional support. We also identify a more radical option, the repositioning of the kinds of outdoor learning that occurs in green school grounds as the basis of teaching and learning in Sterling’s (2004) vision for “sustainable education.” Résumé Dans cet article, nous ex p l o rons des initiatives d’écologisation de cours d’é-
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.054 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".