UNESCO's Teaching and Learning for a Sustainable Future: A critical evaluation of underlying unsustainable progress myths
Bibliographic record
Abstract
The paper examines a representative internet/computer based Education for Sustainability (EfS) learning program aimed at teacher training. Looking at the concept of culture in the program, it provides a critical reading of its implicit and explicit attitudes to science and technology. In order to assess these attitudes, an understanding of science and technology is presented that should form the framework of educational programs committed to sustainability. In addition, the program’s understanding of and attitude towards science is measured against its own criteria. The analysis uncovers a startlingly uncritical attitude to Western science and technology and its associated progress myths, as well as an even more far-reaching, naive advocacy of Information and Communication Technology (ICT) and internet as learning tools for EfS. This not only leads to contradictions with the program’s stated aims and some of its content, but is hardly conducive to sustainability. The paper argues, therefore, that we need a far more critical approach to Western myths of development and scientific progress if we are to encourage a move towards sustainability through education.
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 | 0.123 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.052 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| 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".