Reflections on Applying Institutional Ethnography in Participatory Weight Stigma Research with Young Women
Bibliographic record
Abstract
Inspired by consciousness-raising practices of North American second-wave feminism, Dorothy Smith developed institutional ethnography (IE) as an alternative to established sociology, which she argued objectified people and their experiences. Instead, IE begins from an embodied standpoint to examine how local phenomena are coordinated to happen by ruling relations from afar. In this article, we present methodological insights from our experiences of applying IE, informed by principles of participatory research, in Alberta, Canada to examine the challenges young women (aged 15–21) in larger bodies face while navigating their everyday lives. We begin by exploring current discussions in the burgeoning field of IE, including how IE’s social ontology aligns with participatory approaches to research. Contextualized by our public health backgrounds, we then describe how we used IE to study how the work of growing up in a larger body is socially organized, interpreting work generously as any task requiring thought and intention. Between March-December 2019, we conducted 14 individual interviews and facilitated 5 working group meetings with a subset of interview participants. Discussions during the working group meetings were structured by an adapted critical analysis framework to prompt participants in questioning taken-for-granted assumptions around weight and health. As part of this working group, we developed knowledge mobilization materials (infographics and an open letter) for parents, educators, and healthcare providers about how to navigate weight-related issues with young people, grounded in participants’ experiential knowledge. We specifically reflect on how IE was a valuable tool for addressing four principles of participatory research central to this study: go beyond “do no harm”; provide opportunities for giving feedback; create space for critical engagement; and bring knowledge mobilization to the fore. Overall, our experiences suggest value in IE as a pragmatic, flexible approach to public health research, offering unique methodological tools which keep research participants in view.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".