Making the Path as We Walk It: Changing Context and Strategy on Green Street.
Bibliographic record
Abstract
This is a story about how a national Canadian environmental education program (Green Street) evolved in unpredictable ways and about the particular twist in the road that led program stakeholders to focus on scaling up in different ways than originally imagined. The “twist” occurred when the program reached the initially perceived “peak” in a particular “fitness landscape,” where a focus on policy advocacy (and specifically curriculum reform) seemed a logical next step. However, noting examples within the program of deeper student engagement (with concomitant learning about what constitutes authentic experience); the development of place-based programs that employed extensive and diverse school-community partnerships; the aligning of environmental stewardship with broader notions of active citizenship; and a movement with the potential to engage teachers and stu- dents alike (Quebec’s Brundtland Green School [EVB] movement), the program team instead decided to focus on further development of these and other innovations to generate new models and experiments from which we could all learn. This is the story of a program increasingly participating in a movement-building process rather than focusing on program development and policy advocacy.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.065 | 0.031 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".