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Record W4364351718 · doi:10.1002/jcop.23046

Positionality, intersectionality, power dynamics in community participatory research to define public safety in Black communities

2023· article· en· W4364351718 on OpenAlexaff
Ebony Ruhland, Lauren Johnson, Janet Moore, Cinnamon Pelly, Simone Bess, Jacinda K. Dariotis

Bibliographic record

VenueJournal of Community Psychology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsCommunity Based Research Centre
Fundersnot available
KeywordsIntersectionalityParticipatory action researchCommunity-based participatory researchSociologyPublic relationsCitizen journalismPower (physics)Gender studiesPolitical science

Abstract

fetched live from OpenAlex

Community-based participatory research (CBPR) is necessary for shifting knowledge and empowering community members to establish ownership over research. It was used in this current project to study safety in predominately Black communities. Findings illustrate how the embodiment of power was a present theme and impacted the partnerships among the academics and community, as well as defining "who" could speak on the issues the project was attempting to address. This paper builds upon previous research in CBPR findings to illustrate how community leaders can shape the research, the importance of defining community, and the need to bring to the forefront issues of intersectionality and positionality. In doing so, it attempts to reshape existing CBPR models to better account for the fluid, interactive relationships among the academics, community researchers, and the community leader and expand upon the role of intersectionality in these relationships.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.111
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.585

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1110.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0210.060
Scholarly communication0.0140.015
Open science0.0020.019
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.593
GPT teacher head0.611
Teacher spread0.019 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2023
Admission routes1
Has abstractyes

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