A Comparative Investigation of Environmental Literacy Dimensions in Science Curricula of Several Countries
Bibliographic record
Abstract
Environmental education is necessary to prevent environmental problems. It is useful to analyze the curricula in order to understand the importance given to environmental education. In this study, it was aimed to examine the learning outcomes in Türkiye, Canada (Ontario), Australia, USA (Massachusetts) and England primary science curricula in terms of environmental education and to analyze and compare them according to the dimensions of environmental literacy which are formed knowledge, cognitive skills, affect and behavior. This study was a qualitative study, and the data were collected through document analysis and analyzed through content analysis. In the comparisons made according to the number of environmental outcomes, it was observed that the highest number of outcomes was present in the curriculum of Canada, while the lowest number of outcomes was present in the curriculum of England. All dimensions were found in all curricula except the Science and Technology Curriculum in England, but not all dimensions were equally included in the curricula. In England's curriculum, had no outcomes related to the behavior dimension. The common result was that in all of the curricula, the outcomes in the cognitive skills dimension are more common, while the outcomes in the affective and behavioral dimensions are more limited.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".