Physical Capital and the Embodied Nature of Income Inequality: Gender Differences in the Effect of Body Size on Workers’ Incomes in Canada
Bibliographic record
Abstract
ABSTRACT Cette étude évalue les effets des dimensions du corps – mesurées en utilisant l'index de la masse corporelle – sur le maximum de revenus atteint par les travailleuses et travailleurs au Canada. En utilisant les données d'un échantillon national représentatif des Canadiens, des analyses factorielles montrent que le rapport entre la masse corporelle et le revenu est négatif dans le cas des travailleuses, tandis que dans le cas des travailleurs ce rapport est positif et non‐linéaire. L’étude est basée sur le concept de capital physique défini par Bourdieu et sur le développement de ce concept réalisé par Shilling. On y démontre que ces résultats représentent l'importance relative de la grandeur corporelle dans la production et perpétuation des inégalités des revenus au Canada. This study assesses the effects of body size—measured using the body mass index—on the income attainment of female and male workers in Canada. Using data from a national representative sample of Canadians, multivariate analyses show that, for female workers, the body size‐income relationship is negative. However, for male workers, the body size‐income relationship is positive and nonlinear. Using Bourdieu's conceptualization of physical capital, and Shilling's extension of it, it is argued that these results are suggestive of the relative importance of body size to the production and continuation of gender income inequality in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".