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Record W3089705322 · doi:10.14430/arctic71080

Unpacking Community Participation in Research: A Systematic Literature Review of Community-based and Participatory Research in Alaska

2020· article· en· W3089705322 on OpenAlexvenueno aff
Anuszka Mosurska, James D. Ford

Bibliographic record

VenueARCTIC · 2020
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersNature
KeywordsIndigenousParticipatory action researchSociologyCitizen journalismCommunity-based participatory researchCommunity studiesCommunity developmentEliteInterpretation (philosophy)Public relationsPolitical scienceSocial scienceEngineering ethicsEcologyPoliticsAnthropologyEngineering

Abstract

fetched live from OpenAlex

Although concepts of “community” and “participation” have been heavily critiqued in the social sciences, they remain uncritically applied across disciplines, leading to problems that undermine both research and practice. Nevertheless, these approaches are advocated for, especially in Indigenous contexts. To assess the use of these concepts, we conducted a systematic literature review of community-based and participatory research in Alaska, USA, where social change has been rapid, having ramifications for social organization, and where participatory and community-based approaches are heavily advocated for by Alaska Native organizations. Conceptualizations of community and participation were extracted and analyzed quantitatively and qualitatively. The majority of articles showed a lack of critical consideration around both terms, although this was especially the case in reporting around community. While this lack of critical consideration could lead to issues of local elite co-opting research, an alternative interpretation is that Western sociological literature surrounding community is not transferable to Indigenous contexts.

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)0.032
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
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 head0.687
GPT teacher head0.587
Teacher spread0.100 · 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 designSystematic review
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

Citations32
Published2020
Admission routes1
Has abstractyes

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