ENVIRONMENTAL EDUCATION AND SUSTAINABLE DEVELOPMENT: INCORPORATING ECO-LITERACY INTO THE CURRICULUM
Bibliographic record
Abstract
Environmental education facilitates the implementation of sustainable development through admittance of eco-literacy into education systems. This abstract is focused on the implications of integrating and adapting eco-literacy into education systems to support students’ development of environmentally conscious outlooks. Thus, eco-literacy as knowledge in ecological principles and the skills in their application to relevant problems serves as a appropriate knowledge base for learners. It means that with the inclusion of eco-literacy into the curriculum of educational institutions, the latter will be able to produce environmentally literate individuals competent in the ability to make decisions in favor of the conservation of the planet. This abstract describes several methods and models of how to implement an integration of eco-literacy for different grade levels and makes a stress on interdisciplinary, practical experiences, and community participation. It also covers positive aspects of eco-literacy for instance, inclusion of aspects like sustainable use of resources, environmental awareness as well as the ability to think critically on environmental matters. Moreover, the abstract of the article describes the ways teachers are involved in the formation of their students’ eco-literacy, including the guidance as well as offering the necessary tools and contexts for reflection on the students’ personal carbon footprint. Lastly, by integrating eco-literacy into educational systems, the institutions play a critical role of fostering a sustainable future through instilling in learners the understanding of and attitude towards environmental conservation and Sustainable Development.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".