Rescaling Environmental Governance: The Case of Watersheds as Scale‐Sensitive Governance?
Bibliographic record
Abstract
This chapter focuses on the rescaling of environmental governance – that is, the (re)-drawing of boundaries for the purposes of environmental decision-making. The chapter draws on evidence relating to rescaled water governance in Canada to argue that watersheds, as particular forms of rescaled environmental governance, have increased in popularity, not necessarily because of their inherent naturalness, but because of their status as what Star and Griesemer call boundary objects. The chapter shows how particular features of watersheds – namely their physical size and the shared discursive framings they employ - make the watershed concept both cohesive enough to travel among different epistemic communities, and plastic enough to be interpreted and used differently within them. Finally, the chapter discusses the future research on the rescaling of environmental governance and state–nature relations, as well as the practical implications of this particular form of rescaling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".