Bibliographic record
Abstract
This paper presents highlights from research on community-based water activism in Guelph, Ontario, Canada – one of the most water-rich regions of the world. Social network analysis in conjunction with qualitative methods was used to explore the influence of water activists' normative motivations on the mobilization of collective action on water issues. The findings revealed that even where scarcity is only a remote, long-term threat, decision-making and economic activity involving water have the potential to trigger local citizen action based on a fundamental conflict between the drive of commodification and a countervailing, popularly-held normative consensus concerning water. The findings suggest that a popular consensus that holds water as a commons – and as such, places universal access to water, environmental sustainability, and local community self-reliance ahead of profit making – is an important trigger for collective action when violated by decisions or actions taken in the local community. Understood as such, I draw on scholarship on the moral economy to argue that collective action on water issues in Guelph exposes a unifying theme that connects Guelph's water activists to each other and to a growing worldwide movement to defend the water commons. Moreover, with Canadian social work striving to incorporate matters of environmental justice into its scholarship and practice, in this paper I outline a rationale and identify priorities for social work involvement in this area.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.211 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| 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".