Rescaling Environmental Governance: Watersheds as Boundary Objects at the Intersection of Science, Neoliberalism, and Participation
Bibliographic record
Abstract
This paper is concerned with the rescaling of environmental governance, and with the social construction of environmental and governance scales in particular. With the aid of case-study data from Canada, it is argued that watersheds, as particular forms of rescaled environmental governance, have increased in popularity because of their status as boundary objects: that is, a common concept interpreted differently by different groups. The paper shows how particular features of the watershed approach—namely, their physical size and the shared discursive framings they employ (‘stakeholder’ and ‘integration’)—make the watershed concept both cohesive enough to travel among different epistemic communities, and plastic enough to be interpreted and used differently within them. As such, it is suggested that the trend of the uptake of the so-called ‘watershed approach’ reflects and is shaped by ideologies underpinned by three different, and occasionally competing, epistemic communities: the scientific, neoliberal, and grassroots communities. These arguments corroborate constructivist accounts of the political nature of boundary drawing, bring science into discussion on the relationship between neoliberalism and public participation, and contribute to environmental governance literatures by providing an alternative explanation for the uptake of watersheds in recent decades.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".