Environmental Education and Practices in Canada, Turkey & Pakistan at Primary Level: A Content Analysis
Bibliographic record
Abstract
The current investigation was directed to analyze environmental education and practices in curriculum adopted in Pakistan, Turkey, and Canada at the primary level. For this purpose, General Science textbooks of the public schools in Ontario (Canada), Istanbul (Turkey), and Rawalpindi (Pakistan) were explored and their practical implementation was studied as primary data for the year 2008-2015. A close content analysis of the inscribed curriculum revealed that over time, Canada has taken significant steps to incorporate the elements of environmental awareness as their curricula and trained the teachers to deal effectively with environmental education, leading to creating awareness in problems and their solution among children regarding concepts of waste management, littering and sustainability. Topics related to awareness about reducing, recycle, reuse, and ecosystems were a vital part of the environmental curriculum and practices in Turkey. Exploring climate change and the environment was found to be part of the Science curriculum in Pakistan. However, the practical activities regarding environmental education were relatively less in Pakistan as the implementation of the curriculum is not in its mature stages. Comparison of the study reveals that incorporating environmental projects benefitting from national and international organizations in Pakistan would help as they have contributed constructively in creating awareness in the education sector in Turkey and Canada.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".