Environmental justice and health practices: understanding how health inequities arise at the local level
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it