Community participation in coastal and marine research and monitoring in Inuit Nunangat: a scoping literature review
Bibliographic record
Abstract
In Canada, the participation of Indigenous communities in research and monitoring is growing in response to calls for partnerships and heightened interest in bridging Indigenous and Western science-based knowledge. Yet, as settler scholars, we have noted inconsistencies in the articulation and operationalization of community participation in peer-reviewed literature. We conducted a scoping review of community participation in coastal and marine research and monitoring across Inuit Nunangat. This resulted in 72 studies, most of which were undertaken in Nunavut. Fourteen terms were used to articulate community participation, the most common being: participate, collaborate, community-based, consult, or variations of these terms. Among the studies that used community participation terms, we found that authors only defined terms 10% of the time. Community participation was operationalized primarily through interviews, mapping, and field observations. We assessed studies across a spectrum of community participation levels and found that most studies (81%) reflected minimal levels of participation (i.e., consultative, contractual, and less than contractual). Our results highlight the need for clarity in language use, transparency in reporting research practices, and stronger efforts to support Indigenous leadership and decision-making authority, all of which must be defined on a community or project basis.
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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 | 0.040 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.029 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".