On the ‘Female Gaze’ in the Interview Setting: Methodological Insights From Fieldwork With Women
Bibliographic record
Abstract
Building on a constructivist understanding of the interview techniques common to the social sciences, in this paper I discuss and analyse through a feminist sociological lens the interview setting that I built and experienced during two years of fieldwork with a small sample of Canadian women. Relevant conversational gestures exchanged in such a setting usually encompass verbal and bodily cues, but what principally concerns me here is a further aspect of the interview setting: namely, its visuality, and the related act of gazing carried out by the (female) participants. Using the concept of the ‘female gaze’ (Riley et al., 2016) – i.e., the self-assessing, judgemental gaze that women direct at one another and at themselves in postfeminist contexts – I offer salient examples from my fieldwork in order to show the ways in which the female gaze shaped my understanding of how women look at themselves and at each other (including at me, as interviewer), both in person and in pictures. My goal is to analyse gazing as a competence, and more specifically as a structured and regulated female competence in postfeminist culture, but also to bring a greater reflexivity to bear on the embodied experience of fieldwork (Oakley, 1981; Pillow, 1997). As I came to learn in the course of this project, the act of gazing while conversing, accompanied by the corresponding verbal cues, played a crucial, if unexpected and unplanned, role in data production and in my subsequent choice of research questions.
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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 | 0.051 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.027 | 0.039 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".