The Eco-Scalar Fix: Rescaling Environmental Governance and the Politics of Ecological Boundaries in Alberta, Canada
Bibliographic record
Abstract
This paper engages with recent work in political ecology that explores the ways in which scale is imbricated in environmental governance. Specifically, we analyze the deployment of specific ecological scales as putatively ‘natural’ governance units in rescaling processes. To undertake this analysis, the paper brings two sets of literature into dialogue: (1) political ecology of scale and (2) political economy of rescaling, drawing on theories of uneven development. Building on this literature, we develop the concept of an ecoscalar fix and explore its analytical potential through a case study of the rescaling of water governance in Alberta, Canada. We argue that although the ‘eco-scalar fix’ is usually framed as an apolitical governance change—particularly through the framing of particular scales (ie, the watershed) as ‘natural’—it is often, in fact, a deeply political move that reconfigures power structures and prioritizes some resource uses over others in ways that can entrench, rather than resolve, the crises it was designed to address. Moreover, we suggest that, although watershed governance is often discursively depicted as an environmental strategy (eg, internalizing environmental externalities by aligning decision making with ecological boundaries), it is often articulated with—and undertaken to address challenges that arise through—processes of uneven development.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".