Bibliographic record
Abstract
Recently, scholars have used a Bourdieusian theory of practice to analyze systems of sexual stratification, including an examination of sexual fields and sexual (or erotic) capital. While the broad structural features of the sexual field have been a point of focus in this latter research, a systematic analysis of the interactional processes that operate within the sexual status order has not been performed. In this paper, drawing on original data from an urban gay enclave, I identify six key interactional processes that occur within sexual fields, including: 1) actors’ recognition that the sexual field is constituted by a set of relations anchored to competition and sexual selection; 2) the perception of a generalized other ( Mead 1934 ) within the field, including knowledge concerning a given field’s collective valuations of sexual attractiveness; 3) a formulation of one’s own position within the sexual status order vis-à-vis intersubjective feedback and the development of a looking-glass self ( Cooley 1902 ); 4) an assessment of others’ positions within the sexual status order; 5) knowledge of “the game” ( Goffman 1959 )—including how to conduct a successful self-performance (ibid.), the construction of an optimizing front (ibid.) and proper field-specific demeanor ( Goffman 1967 ); and finally, ideally, 6) the ability to “save face.” In total, these interactional processes draw from and reproduce systems of sexual stratification, and are likely to generalize across sexual fields.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".