Embodied Reflexivity in Qualitative Analysis: A Role for Selfies
Bibliographic record
Abstract
This article introduces a case study on the use of selfies as a means to support embodied reflexivity in phenomenological research. There is a recognized need to make reflexive practice in qualitative health research more transparent. There is also a move towards an embodied type of reflexivity whereby researchers pay attention to their physical reactions as part of the research process. Being reflexive is especially challenging when researchers work in teams rather than as individuals, and when researchers and participants do not meet because data collection and analysis are separate from one another. We used FINLAY's (2005) model of reflexive embodied empathy to explain how taking selfies allowed an international team of researchers to engage reflexively with a participant when their primary access to her lifeworld was an interview transcript. Key concepts from FOUCAULT's (1988) theory of technologies of self, critical self-awareness and self-stylization, shed light on this phenomenon. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1702124
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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 | 0.436 | 0.388 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.014 | 0.129 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".