The promise and pitfalls of community-based monitoring with a focus on Canadian examples
Bibliographic record
Abstract
Community-based monitoring (CBM) is a widely used form of scientific data collection in which local community members directly participate in ecosystem research and also help the process by sharing traditional ecological knowledge and local understanding of land and resources. This paper presents a review of the challenges and opportunities associated with CBM projects in Canada and internationally. While Canadian cases are the primary focus, international examples are drawn on to provide additional context. Based on our review of 121 documents and publications, we found that CBM helps fill science research gaps by providing access to continuous data sets on the ecosystems being studied. CBM also increases the credibility of the data among users, as the community itself takes part in the environmental monitoring process. CBM supports cross-cultural learning and the co-production of knowledge by using traditional ecological knowledge with science, thereby enabling researchers, scientists and community members to learn from one another. Our review suggests that although there are multiple successes, CBM faces several challenges that constrain its progress including funding shortages, lack of support for local stewardship, and inadequate training for local users in the operation of equipment and data collection methods. Data sharing and rights on the use of data are also constraining to the long-term success of CBM programs.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".