Pan-Canadian review of community-based monitoring projects and their capacity to enhance environmental monitoring programs for cumulative effects assessments
Bibliographic record
Abstract
While community-based monitoring (CBM) can support meaningful participation of the public in environmental decision-making, it remains unclear if and how CBM can support western science approaches to biophysical studies within cumulative effects assessment (CEA). We scored 40 Canadian CBM projects on their ability to enhance CEA's western science approaches to environmental monitoring. We used multivariate analyses to determine if the highest-scoring projects shared characteristics that could inform the design of CBMs to support CEA. Cluster analysis and non-metric multi-dimensional scaling ordination revealed that highest-scoring projects were distinct from lower scoring projects, and the Similarity Percentages Routine identified characteristics that differentiated these projects. The highest-scoring projects involved non-profit organizations as bridging organizations that coordinated community participation and received funding and in-kind support from provincial/territorial government agencies. Participants in these projects collected measurements and samples using standardized protocols described in training manuals. Their data were publicly accessible in georeferenced databases and were used for baseline studies and resource management. There are existing CBM projects in Canada that thus appear well positioned to enhance western science approaches to CEA. Further study is required to identify how CBM projects can be designed to braid Indigenous and western science approaches to mutually enhance CEA methods.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.038 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| 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".