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Record W4285235191 · doi:10.1139/facets-2021-0067

Community participation in coastal and marine research and monitoring in Inuit Nunangat: a scoping literature review

2022· article· en· W4285235191 on OpenAlexafffundvenueabout
Arden Drake, Aleksandra Perković, Connor Reeve, Steven M. Alexander, Vivian M. Nguyen, Karen M. Dunmall

Bibliographic record

VenueFACETS · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsFisheries and Oceans CanadaCarleton University
FundersFisheries and Oceans Canada
KeywordsOperationalizationIndigenousCommunity engagementCLARITYCommunity participationCitizen scienceTransparency (behavior)Public relationsExploratory researchPeer reviewPolitical scienceSociologyEnvironmental planningGeographySocial scienceSocioeconomicsEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.179
GPT teacher head0.512
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2022
Admission routes4
Has abstractyes

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