Letters from a dying college: How the climate crisis demands a wilder pedagogy and wilder policies
Bibliographic record
Abstract
This paper takes the academically unorthodox form of personal correspondence. This method, of letters between two educators writing to one another across the distance of two continents and different experiences, seeks to create an inclusive, confessional tone, one that invites the reader to get closer to the lived experience of those struggling within the educational and environmental crises. Critically, this correspondence also seeks to open discussion about the difficult demands of state secondary and tertiary education. The authors explore issues regarding their denuded experiences of working in formal education settings while bearing witness to environmental degradation and ecological collapse. In light of their exploration, the authors argue for an ‘agrios’, a wilder, more expansive polis, coupled with more ecologically-inclusive governance, to address the current potentially catastrophic political leadership that has seemingly turned away from ecological responsibility. This paper culminates in direct letters that focus on a series of practical proposals for action and on four premises for developing agriocy – the policy that supports the agrios/agriocity.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.019 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".