Bibliographic record
Abstract
Education plays multidimensional role since origin. No field remains untouched with the glory of education by itself being a renowned field. It is the means the attain our final goal in life. Education, creativity and innovation are the keys in the hands of human being to unlock any locked situation. Ever concerned issue of climate change is the focus of this article and researcher tries to establish its relation with education. Climate change is a phenomenon occurs naturally with very low rate but rises unevenly due to human activity. Quoto protocol, montreal protocol, Paris agreement e.t.c are few governmental efforts put forth to reduce the CO2 emission globally and maintain sustainability. The concept of climate change is recognise decade ago and trying to increase the awareness towards it but the current scenario shows that it is quite low. People are still careless about climate changes that occurring due to human activity which needs to reduce through proper educational guidance via diverse mitigation and adaptation strategies. This is possible only through promotion of climate change education. As a researcher we find a gap between knowledge and practice of human beings regarding climate change. So involving climate change education in curriculum is worthy in all avenues. This article talks about climate change, climate change education, various roles the education going to play in the field of climate change. Key words: climate change, climate change education, mitigation and adaptation strategies, climate change action, sustainability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".