Living in the Shadow of an "Obesity Epidemic": The Discursive Construction of Boys and Their Bodies
Bibliographic record
Abstract
This dissertation is about boys and fatness. In it I explore the central discourses that shape young men’s (13-15 years) experiences of their bodies, particularly in relation to body size, shape, and fatness. A central objective is to listen, hear, and take seriously the embodied health rationalities of young men as they negotiate the multiple and contesting discourses that confront them in their daily lives. I employ a feminist poststructural lens to account for the nuanced, alternative, and contextually specific ways young men think about and do health. Data collection was divided into three phases (non-participant observation, photo(focus) groups, and interviews) and was implemented at two Toronto area sites, including an exclusive private school and a publicly funded parks and recreation community centre. I demonstrate that there is not one way of experiencing fatness and masculinity, rather the young men’s constructions of fatness and health were fluid, shifting, contradictory and cross cut by other salient identity categories such as gender, race, ethnicity, social class, and age. Using Michel Foucault’s concept of governmentality, I show how obesity discourse provides a set of resources by which young men are able to construct themselves as autonomous, rational, neoliberal subjects, and how these subjectivities are differentially constituted depending on social and cultural positioning. I also reveal how differently raced and classed young men take up and embody normative ideals of the lean muscular male body through culturally appropriate masculine technologies of the self (i.e. sport and heterosexuality). The multiplicity of health and body discourses available to the young men gave rise to contested and ambivalent experiences and practices, such that dominant discourses were not always articulated in a straightforward and predictable manner, but were imbued with alternative and, in some cases, subversive meanings. To date, the social sciences have neglected to account for the relationship boys and men have with fatness discourses. By centering the analysis on the embodied experiences of diverse racialized and classed youth, this research demonstrates that weight and shape is more than a biomedical problem to be eradicated, but a discursively compelled embodiment that exists at the crossroads of the social, cultural, psychic, and biologic.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".