Does community-based monitoring advance Indigenous self-determination? Inuit-led monitoring and governance in Nunavut and Greenland
Bibliographic record
Abstract
Existing literature on Community-Based Monitoring suggests that participation in monitoring can increase the extent to which decision-making is informed by observed environmental trends. Yet, there is an ambivalence within the literature concerning the value for Indigenous peoples. Some scholars maintain that CBM programs replicate and reinforce colonial political inequalities while others suggest that such programs can and do support Indigenous self-determination. In this study, I explore such questions through empirical engagement with case studies of two established Indigenous-led programs in Nunavut, Canada, and Greenland that involve the collection of Indigenous Knowledge for use in decision-making. I contribute to the field by examining monitoring as a process through which knowledge and governance are co-constituted through politically unequal relationships. Considering this, I argue that Indigenous-led CBM can support self-determination in environmental governance given the right conditions. I identify three factors that are fundamental to achieving this. First, explicit legal acknowledgement of Indigenous rights, authority, and knowledge systems is key to mobilizing CBM data. Second, while the fundamental goal of such programs is to enhance the use of knowledge in decision-making, Indigenous leadership and data governance are important safeguards against extractive knowledge production. Finally, a theory of power is necessary to critically analyse both the directly observable and more subtle ways in which power influences the potential for CBM programs to promote Indigenous self-determination. • Systematic collection of Indigenous Knowledge can produce valuable datasets. • Rigour in data collection does not automatically translate to improved decision-making. • Neither CBM nor governance are merely technical, politically neutral processes. • Indigenous leadership and governance help counter extractive knowledge production. • Unequal politics continue to limit Indigenous self-determination in decision-making.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".