Knight moves: four political scenarios for ecological upswing jumping off pragmatic sociology
Bibliographic record
Abstract
In the context of rising anti-environmentalism and accelerating ecological crisis, this article imagines four kinds of resurgent environmentalism or ‘ecological upswing’, rooted in the political traditions of the far left, left, right and far right. Building on work by pragmatic sociology to apprehend ecological worth, I conceptualize ecological upswing as a diagonal departure away from hegemonic market worth and ever-deeper individualism. In contrast to previous approaches distinguishing a ‘green’ common good as a novel order of worth that stands apart from more conventional common goods, I explore two forms of ecological upswing that slowly emerge from within conventional common goods as well as two forms that pose a more ‘serious readjustment’ to the whole model. Taking examples from Canada, my analysis of each ecological upswing includes speculative scenarios in which society shifts away from neoliberal individualism towards the common good via tipping points of nonlinear, cascading transformation into less anthropocentric imaginaries of moral worth. Drawing on the metaphor of a knight’s move in chess, or a creative detour that challenges linear, modern ideas about progress, this article charts four different ways in which ascendant forms of ecological worth could swing a wealthy western society like Canada towards multispecies flourishing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.075 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".