Campfire Sessions as Contemplative Intervention to Facilitate the Redesign of Education for Regenerative Systems Change
Bibliographic record
Abstract
The world today is facing sustainability challenges that will ultimately lead humanity into a state of deep crisis. This state of the world requires an educational transformation in today’s context. There is no shortage of possible alternative educational practices to meet the needs of a society in transition. However, there is a lack of knowledge and skills on how to implement these practices in the educational system in a sustainable way, with plenty of internal and external resistance. The field of education is experiencing a lack of capacity and willingness to respond to the demands of our time by successfully initiating forms of systemic change. This initial study helps us to explore contemplative campfire sessions as a vehicle for educational change, facilitating slowness, dialogue and space for a solidly built new structure. To this end, we explored metaphorical campfires as sites for micro-phenomenological interventions inspired by the principles of regenerative education. These sessions were co-created with Built Environment teachers who are in the process of redesigning their curriculum for the transition challenges facing cities (e.g. increasing numbers of people living in cities, energy transition, smart integration of water, issues of mobility). It is too early to claim that contemplative bonfires are the ingenious solution to our transition problems, but our exploration has shown promising results in prioritising, designing and implementing educational change issues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".