Gender, Field, and Habitus: How Gendered Dispositions Reproduce Fields of Cultural Production
Bibliographic record
Abstract
Bourdieu argues that fields of action produce a specific habitus in participants, and views this specific habitus as a mechanism through which the field is reproduced. Although Bourdieu acknowledges the habitus as gendered, he does not theorize gender as part of the mutually constitutive relationship between field and habitus. Using evidence from two cultural fields, the Toronto heavy metal and folk music scenes, I show that gender is central to the process through which field and habitus sustain each other. The metal field produces a “metalhead habitus” that privileges gender performances centered on individual dominance and status competition. In contrast, the “folkie habitus” encourages gender performances centered on caring, emotional relations with others, and community‐building. These differently gendered habitus support different working conventions: music production occurs largely through volunteer‐based nonprofit organizations in the folk field, and individual entrepreneurship in the metal field. The gendered habitus also supports different stylistic conventions: guitar virtuosity in the metal field, and participatory music‐making in folk. Applying a gendered lens to the field–habitus relationship clarifies the mechanisms through which cultural fields shape individual action, and the mechanisms through which cultural fields are reproduced and maintained.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".