Designing Education for Eco-Social–Cultural Change
Bibliographic record
Abstract
Abstract This paper involves the unlikely partnering of a designer/design educator and an environmental philosopher of education as they consider together pedagogical responses to the metacrisis. It will begin with an exploration of some recent research that positions education at the heart of the project of eco-social – cultural change. Then, using six prompts proposed as a starting place for this type of education we will follow a full semester of a third-year undergraduate design class as students are immersed in a curriculum created with a vision towards both eco-social – cultural change and, by implication, ‘doing design differently’. Through this reflective study, the research hopes to explore some of the successes, failures, learnings, and potential challenges that exist for students, educators, and theories of educational change in the work of educating in, through, and beyond these times of crisis. The paper will end with a rendering of our findings and an extended discussion of the pedagogical possibilities, prompts, and peculiarities of teaching during this metacrisis and some considerations around the potentialities and limitations of these six prompts for eco-social – cultural change and environmental 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".