Feminist Reflections on the Relation of Emotions to Ethics: A Case Study of Two Awkward Interviewing Moments
Bibliographic record
Abstract
In Canada, social scientists are accountable to ethical guidelines, including the minimization of harm. Simultaneously, they are accountable to an academic community. But what of those moments in the researcher-participant relationship when these principles clash? They have at times done so resoundingly in our careers as qualitative interviewers, especially when we sought to ensure that information we implicitly understood and perceived as crucial would be duly stated by participants for the research record. Such attempts gave rise to deeply awkward interactions rife with emotions that even risked the premature termination of the interviews. In this article, we use methods from a feminist paradigm, and specifically standpoint and discursive positioning theory, to reflexively analyze the ethics in practice surrounding two of our own cases of awkward moments. Our analysis illustrates how the emotions of awkward moments can be symptomatic of everyday ethical conundrums. We particularly consider whether and how our engagement in reflexivity from these two vantage points can mitigate any real or imagined harm. We indicate how the understanding we develop from our analysis can lead to proactive recommendations for researchers to engage with their emotions and conduct themselves more ethically, both in the field and in analyses.
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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 | 0.038 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.044 | 0.019 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".