Bibliographic record
Abstract
In this E4E (Educating for Environment) pilot project we explored the delivery of inquiry-based environmental education within a small, rural elementary school in Ontario, Canada, using a school-as-a-community model, rather than the more common single grade model. At the outset of the project we wondered if environmental education could be significant in building social capital, that is, building a stronger school community, and also, if the strong school community would respond to their experience with enhanced pro-environmental attitudes and behaviours. Since this paper is based on a small pilot project, not all of our wonderings were realized, yet we are encouraged by our findings and see environmental education as much more than a discrete set of lessons in a science curriculum within a school building. Qualitative data comprising of interviews and a focus group was collected at the completion of the two-week project. Analysis points to benefits in the form of increased understanding of and for the environment, and strengthening of social capital within the school, both of which support the development of environmentally and socially conscientious citizenship amongst participants.
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".