Trends and key elements in community-based monitoring: a systematic review of the literature with an emphasis on Arctic and Subarctic regions
Bibliographic record
Abstract
Community-based monitoring (CBM) is receiving much attention from the research community, particularly in Arctic and Subarctic regions of Canada and other circumpolar regions. Currently, there is a lack of understanding of the trends and patterns in its use within the literature and a documented need to improve environmental CBM efforts in the Arctic and Subarctic regions. A systematic literature review was conducted of CBM publications in peer-reviewed and grey literature to provide a synthesis of trends on the topic and to clarify key elements that are needed to operate an environmental CBM program in Arctic and Subarctic regions. Both sets of literature show a significant growth in the publication of CBM studies over time, with a high proportion of research taking place in North America and in the field of environmental sciences. More CBM studies are reported in connection to First Nations and Inuit groups, as compared to other Indigenous groups. Thirteen key elements of environmental CBM programs, commonly reported in the literature focused on Arctic and Subarctic regions, were identified in the analysis. Specifically, traditional and local ecological knowledge (TLEK) was a unique component highlighted in Arctic and Subarctic sources and a specific feature observed in studies focusing on Indigenous groups. The identification of such key CBM elements serves as a resource to guide current and future environmental CBM initiatives in northern regions and elsewhere. Future research on this topic should contrast and compare literature findings with existing environmental CBM programs and provide more case studies to show the process and utility of environmental CBM initiatives in the Arctic and Subarctic, particularly with use of TLEK and the ways to facilitate it within a CBM program.
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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 | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.024 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".