Environmental and geographical education for sustainability : cultural contexts
Bibliographic record
Abstract
Geography, Environment, Sustainability, Culture and Education Culturing Sustainability: Towards Frameworks of Understanding Culture and Contexts Childhood, Peer Culture and Environmental Learning Orientations and Approaches to Environmental and Geographical Education for Sustainability Approaches to Learning and Teaching Environmental and Geographical Education for Sustainability 'Whole School' Approaches to Sustainability Beyond the Separate Subjects: Towards a Post-Disciplinary Approach to Environmental Education Education for Sustainability: Schools and their Communities Eco-Schools and Green Schools Fieldwork and Outdoor Education for Environmental and Geographical Education for Sustainability New Technologies: Contributing to New Concepts and Cultures of Curriculum and Sustainability Approaches to Environmental and Geographical Education for Sustainability in Finland Australia Environmental Education and Cultural Change in a Land of Plenty Environmental and Geographical Education for Sustainability in Hong Kong and China: Trends and Environmental Education for Sustainability in Korea Culturing Education for Sustainability: Experience in Arctic Canada Approaches to Environmental and Geographical Education for Sustainability in the United States Approaches to Environmental Education for Sustainability in India Approaches to Environmental and Geographical Education for Sustainability in Brazil Enabling Environmental and Sustainability Education in South Africas National Curriculum: Context, Culture and Learner Aspirations for Agency Index.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".