Environmental justice and health practices: understanding how health inequities arise at the local level
Bibliographic record
Abstract
While empirical evidence continues to show that people living in low socio-economic status neighbourhoods are less likely to engage in health-enhancing behaviour, our understanding of why this is so remains less than clear. We suggest that two changes could take place to move from description to understanding in this field; (i) a move away from the established concept of individual health behaviour to a contextualised understanding of health practices; and (ii) a switch from focusing on health inequalities in outcomes to health inequities in conditions. We apply Pierre Bourdieu's theory on capital interaction but find it insufficient with regard to the role of agency for structural change. We therefore introduce Amartya Sen's capability approach as a useful link between capital interaction theory and action to reduce social inequities in health-related practices. Sen's capability theory also elucidates the importance of discussing unequal chances in terms of inequity, rather than inequality, in order to underscore the moral nature of inequalities. We draw on the discussion in social geography on environmental injustice, which also underscores the moral nature of the spatial distribution of opportunities. The article ends by applying this approach to the 'Interdisciplinary study of inequalities in smoking' framework.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".