Political education in the climate crisis: Elaboration and reinvigoration of the agonistic model
Bibliographic record
Abstract
The paper argues that the climate crisis is a political issue, not merely a scientific issue, and that agonistic democratic theory (Mouffe) remains helpful to underpin political education. It offers three elaborations that support this argument. The first is on how the category of the “enemy” in Mouffe’s work does or does not play a role in political education. The second addresses the misinterpretation of agonistic political theory as requiring the active fostering of conflict. The third elaboration is on how antagonism can be transformed into agonism. On this point, the paper argues that agonistic political education involves the uncoercive rearrangement of the desire (Spivak) to eradicate an enemy into the desire to contest a political adversary in democratic struggle. It concludes by discussing the climate crisis as a frontier of struggle that ought to play a prominent role in political education today, and that illustrates the viability of agonistic democratic education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".