Participant Reflexivity in Community‐Based Participatory Research: Insights from Reflexive Interview, Dialogical Narrative Analysis, and Video Ethnography
Bibliographic record
Abstract
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.
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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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.127 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".