The Diluted Curriculum: The Role of Government in Developing EcologicalLiteracy as the First Imperative in Ontario Secondary Schools
Bibliographic record
Abstract
In 2000, the Ontario Ministry of Education removed Environmental Science from the secondary school curriculum as single-focus, stand-alone courses. Instead, the Ministrychose to integrate or “infuse” ecological concepts in other science and geography courses. In this study, surveys were sent out to science and geography teachers across theprovince. Teachers were asked whether or not they taught various topics, how much time they spent teaching these topics, and how much time they spent per course teachingoutdoors. The data collected from the surveys demonstrate that grade 9/10 and grade 11/12 science and geography teachers are, in fact, spending very little time teachingecological concepts. There is a limited and ineffective emphasis on learning about environmental science topics or promoting ecological literacy in the current curriculumguidelines. The results of the study indicate the failure of the “infusion model” for ecological education. The study suggests that in light of the serious challenges theecosphere faces in the future, ecological literacy must become the first imperative in the school curriculum.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".