'SEEDS' of 'Good Lessons' through 'Many a Drop'-- Media Initiation in Environmental Education: An Indian Model of Environmental Pedagogy
Bibliographic record
Abstract
Environmental communication is now an emerging and a significant curriculum from schools to research centers. The effective and efficient environmental communication occurs when learners interact with their surrounding environment/ecology in which they live and reciprocate for sustainable protection and restoration of it. Developing countries in Asia and Africa are now setting up new role models and practices in curricula of environmental communication. The traditional theory based environmental communication curriculum of the last century is now actively investigated and restructured through community based learning, affirmative actions, and student centered participatory curriculum. Kerala, a southern State in India, serves as an exemplar of this new eco-venture. Through case studies like, Nalla Paadam (Good Lesson), Palathulli Project (Many a Drop Project) by the Malayalam language daily ‘Malayala Manoram’, and SEED project by another Malayalam daily ‘Mathrubhumi’, this paper analyses the innovative curriculum practices in the state of Kerala in India.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".