Colonialism in Community-Based Monitoring: Knowledge Systems, Finance, and Power in Canada
Bibliographic record
Abstract
Community-based monitoring (CBM) programs are increasingly popular models of environmental governance around the world. Accordingly, a handful of review papers have highlighted the various benefits, challenges, and governance models associated with their uptake. These reviews have been pragmatic in their recommendations and have supported CBM scholars and practitioners in implementing and understanding the various possible forms of CBM, but they have largely been silent on issues around the power dynamics implicit in CBM. Structured around explorations of the colonial politics of knowledge, funding, and finance, this article argues that dominant knowledge systems—specifically those that underpin Western, colonial governments and liberal, capitalist economies—shape the provisioning of funding for local programs and determine the significance of different types of community observations in shaping management decisions. To make this argument, we situate our work at the intersection of political economy and knowledge systems, using theoretical insights and empirical examples to show that funding and finance are key sources of power in shaping CBM programs. These are important insights because CBM is often framed as a purely scientific—and therefore politically neutral—activity. Through this work, we explore questions of intellectual property, histories of institutional exclusion and the privileging of certain knowledge systems, and the relationships of trust and mistrust across different groups and authorities, with the aim of stimulating critical discussions on the power relationships in CBM that will be useful to scholars and practitioners.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".