Interviews as encounters: issues of sexuality and reflexivity when men interview men about commercial same sex relations
Bibliographic record
Abstract
Few qualitative sociologists have considered how men who have sex with men hold diverse understandings of sexuality and how these matter in research encounters, especially as it regards ‘touchy’ interview topics such as intimacy, intercourse and men’s bodies. Drawing from transcripts and field notes concerning my experiences of interviewing 30 male-for-male internet escorts in Montréal, Ottawa, Toronto (Canada), Houston and New York (USA), as well as London (England), I analyse moments where, as the interviewer, I was sexualized by respondents. A first question was often posed to me at the start of interviews: ‘ are you gay?’ The ‘ are you gay?’ question not only seeks out a singular identity declaration but also flips over established researcher-respondent roles, indicating that the reflexivity of the respondent is as important as the reflexivity of the researcher in shaping the conversation to come. My analysis demonstrates why it is important to consider the impact of researcher bodies and speech acts during interviews. Arguing that there are specificities of talk and gesture concerning queer sexualities that researchers must be aware of during interviews, I focus on how my responses to respondent propositions and sexualization shaped and modified the meanings produced through the research encounter.
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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 | 0.091 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.023 | 0.054 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| 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".