Creative Producers and Gender Relations: A Field Analysis of Two Grassroots Music Scenes
Bibliographic record
Abstract
This dissertation uses a comparative case study of two grassroots music scenes—the folk music and heavy metal scenes in Toronto—to examine gender relations among cultural producers. I collect data using semi-structured interviews with 63 field actors, 70 instances of participant-observation, and discourse analysis of key public texts. Building on Bourdieu’s field theory, I argue that gender organizes fields of cultural production, including (1) the field’s economy of symbolic capital (2) the connection between field and habitus and (3) the spaces where musicians develop the embodied cultural capital required for music careers. The first paper shows that field organization impacts the extent to which field members’ gendered dispositions produce symbolic capital, or reputation. Two features of cultural fields shape whether symbolic capital is gendered: the degree to which symbolic capital is institutionalized, and the level of symbolic boundary-drawing in the field. The metal field’s low institutionalization of symbolic capital and high boundaries foreground gender as a basis of symbolic capital, while the folk field’s high institutionalization of symbolic capital and low boundary-drawing reduce the extent to which gender matters. The second paper situates gender as central to relationship between field and habitus. Participants in the metal field develop a metalhead habitus that privileges gendered practices centered on individual dominance and status competition, while the folkie habitus encourages gendered practices centered on caring, emotionality, and community-building. These gendered habitus support different working conventions: volunteer-based non-profit organizations in folk, and individual entrepreneurship in metal. The gendered habitus also supports different stylistic conventions: guitar virtuosity in the metal field, and participatory music-making in folk. The third paper finds gendered access to the learning spaces where musicians develop performance capital, a form of embodied cultural capital denoting the instrumental and interpersonal skills required to perform music. Folk’s learning spaces are largely public and do not require social networks for access, while heavy metal’s learning spaces are private and centered on male-dominated friendship networks from which women are often excluded. These different learning spaces creates gendered patterns of access to the embodied cultural capital required to develop a music career.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".