Is Degrowth Education an Alternative in the Minds of Educators in the Face of the Serious Eco-Social Crisis and Global Warming?
Bibliographic record
Abstract
The aim of this research is to find out whether education students and professionals are aware of the seriousness of climate change and the environmental crisis and whether they receive training to deal with it in their professional future. More specifically, this study aims to analyze if they are aware of the degrowth proposal and consider they should train themselves and future generations in it to tackle this ecosystemic crisis profoundly. The methodology used was qualitative, through focused semi-structured in-depth interviews. The results of the data analysis, carried out with Atlas.ti, are structured around four dimensions: (a) Climate change, sustainability-consumption-social implications, (b) Growth, degrowth, collapse, (c) Personal attitudes towards caring for the planet and (d) Educating/training for degrowth. It is concluded that there is a general awareness concerning degrowth as a relevant issue and a possible alternative, but this is not applied in educational and curricular practice. The need to review the initial training plans for future teachers to introduce these elements is discussed. A limitation of this study is the scarcity of literature on degrowth in education and the need to expand the research sample in order to generalize the findings obtained in the research.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".