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Record W2109164441 · doi:10.1002/casp.2227

Participant Reflexivity in Community‐Based Participatory Research: Insights from Reflexive Interview, Dialogical Narrative Analysis, and Video Ethnography

2015· article· en· W2109164441 on OpenAlexaff
K. Wayne Yang

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReflexivityDialogical selfEthnographyParticipant observationNarrativeSociologyCitizen journalismParticipatory action researchNarrative inquiryQualitative researchPsychologySocial psychologyAnthropologyArtPolitical science

Abstract

fetched live from OpenAlex

Abstract Focusing on researchers, the predominant discourse on reflexivity has seldom considered the contribution that participants could make to research through their self‐reflections. To bring to light the significance of participants' self‐reflection in participatory inquiries, I develop the concept of participant reflexivity, referring to the process in which participants use insights gained through self‐reflection for data analysis and group discussion. My discussion is based on a community‐based participatory research project conducted with a group of adult learners on their educational experiences. I examined the accounts shared by one of the participants by using insights from the theories of reflexive interview, dialogical narrative analysis and video ethnography, and found that her accounts played a pivotal role in evoking group reflections and drawing the conclusion of the project. I argue that participant reflexivity is a useful construct that can do justice to what participants can uniquely offer in participatory inquiries. The concept can also contribute to advancing knowledge of reflexivity by complementing the researcher‐focused predominant discourse on reflexivity. Copyright © 2015 John Wiley & Sons, Ltd.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.127
metaresearch head score (Gemma)0.117
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.673

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1270.117
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0110.045
Scholarly communication0.0130.019
Open science0.0040.014
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.925
GPT teacher head0.710
Teacher spread0.215 · 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

Labeled directly by 2 models reading the full record.

Science and technology studiesMetaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative
DomainMethods
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

Citations26
Published2015
Admission routes1
Has abstractyes

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