Bringing an Equity Lens to Participant Observation in Critical Ethnographic Health Research
Bibliographic record
Abstract
Critically-oriented health research often engages participants whose lives are shaped by structural inequities and structural violence. As scholars who engage in critical theoretical, praxis-oriented research, including research with social justice and decolonizing aims, we are cognizant of the histories of exploitation and structural violence often perpetuated through research. To engage in research that effectively promotes health equity, we are increasingly aware of the necessity of critical research approaches that include processes for engaging in data collection that are respectful, affirming, and minimize harm, and that illuminuate unequal relations of power, challenge the status quo, and contribute to social change. The aim of this paper is to explore participant observation as a method of data collection in critical ethnographic health research with people impacted by structural violence and inequity. Our premise is that it is not possible to conduct research that exposes structural violence, marginalization and social injustices without also critically examining our research processes. To illustrate, we weave together our experiences of conducting critical ethnographic work in diverse contexts to examine the complexities of conducting participant observation with people impacted by structural violence, surfacing the tensions between the potential for harm in research, and strategies for promoting equity. Specifically, we present our collective analysis of how observational practices can reproduce stigma, exacerbate harms associated with methodological and academic colonialism, thereby contributing to epistemic violence, and how participant observation can be deployed in ways that prevent and mitigate such harms. Despite the inherent challenges and complexities, we see immense value in critical ethnographic research that includes participant observation, and we join others in advocating for trauma-, violence-, and justice-informed approaches to critical ethnographic research.
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How this classification was reachedexpand
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 | 0.138 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.097 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".