The Contributions of Community-Based Monitoring and Traditional Knowledge to Arctic Observing Networks: Reflections on the State of the Field
Bibliographic record
Abstract
Community-based monitoring (CBM) in the Arctic is gaining increasing support from a wide range of interested parties, including community members, scientists, government agencies, and funders. Through CBM initiatives, Arctic residents conduct or are involved in ongoing observing and monitoring activities. Arctic Indigenous peoples have been observing the environment for millennia, and CBM often incorporates traditional knowledge, which may be used independently from or in partnership with conventional scientific monitoring methods. Drawing on insights from the first Arctic Observing Summit, we provide an overview of the state of CBM in the Arctic. The CBM approach to monitoring is centered on community needs and interests. It offers fine-grained, local-scale data that are readily accessible to community and municipal decision makers. In spite of these advantages, CBM initiatives remain little documented and are often unconnected to wider networks, with the result that many practitioners lack a clear sense of the field and how best to support its growth and development. CBM initiatives are implemented within legal and governance frameworks that vary significantly both within and among different national contexts. Further documentation of differences and similarities among Arctic communities in relation to observing needs, interests, and legal and institutional capacities will help assess how CBM can contribute to Arctic observing networks. While CBM holds significant potential to meet observing needs of communities, more investment and experimentation are needed to determine how observations and data generated through CBM approaches might effectively inform decision making beyond the community level.
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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 | 0.043 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.061 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| 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".