Connecting community-based monitoring to Arctic environmental decision-making and governance: A systematic scoping review of the literature
Bibliographic record
Abstract
Arctic community-based monitoring (CBM) programs have proliferated in recent decades. While the desire to influence decision-making is frequently listed as a motivation for CBM, there is a dearth of literature examining whether and how this goal is achieved in the Arctic. We draw on a systematic scoping literature review to examine the current state of the literature on Arctic CBM and environmental decision-making. Relevant articles ( n = 27) were identified through inclusion/exclusion criteria (i.e., English language, peer reviewed, published between 1991 and 2021, and based on primary research) and analyzed using a data extraction questionnaire. We find that there is a growing focus on the relationship between Arctic CBM and decision-making in a range of decision contexts, most notably including co-management institutions. We note that less attention was paid to the potential effects of the often unequal, settler-colonial politics within the broader environmental governance system on the relationship between CBM and decision-making. Indigenous peoples and Indigenous Knowledge systems play a significant role within the included references, but less than half of the included references incorporated Indigenous governance concepts to a major extent. Based on our findings, we recommend future studies engage critical analysis of the influence of the governance and politics in the Arctic (1) on environmental decision-making; (2) the politics of knowledge; and (3) the use of digital technologies in the collection, storage, and mobilization of CBM data.
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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 | 0.044 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".