View From the Penthouse: Epistemological Bumps and Emergent Metaphors as Method for Team Reflexivity
Bibliographic record
Abstract
Research in health care occurs within interdisciplinary teams that include clinician-researchers who have multiple epistemological orientations. Rigor in collaborative projects requires reflexive attention to how the paradigmatic questions raised by diverse epistemological orientations, and the ethical stances of each researcher, shape findings. This methodological article draws on three events during an ethnography of stigma in psychiatry to define and illustrate how we used double hermeneutics in data analysis. This allowed us to examine the metaphors that emerged from what we are conceptualizing as "epistemological bumps." This heightened the team's awareness of the epistemological horizons and mixing that occurred, as well as revealing what mattered to each researcher, during the crafting of our research decisions and findings. We argue that interdisciplinary research on complex processes in health care requires this close examination of team experiences and moral stakes during collaborative analysis, and offer conceptual suggestions for reflexivity and rigor.
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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 | 0.284 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".