Community wellbeing: The impacts of inequality, racism and environment on a Brazilian coastal slum
Bibliographic record
Abstract
This article applies the 3-dimensional wellbeing lens (based on material, relational and subjective dimensions of wellbeing) to examine the factors that affect wellbeing in a slum community (Vila dos Pescadores, in the city of Cubatão, Southeast Brazil). This wellbeing framework proves useful in understanding how community wellbeing is impacted by several negative factors: the perceptions of slums, the presence of systemic racism and growing inequality, and a range of environmental impacts arising from industrial and urban pollution, and environmental disasters. Within this mix of environmental and social impacts are links between poverty and exposure to environmental hazards, and effects of environmental racism. On the positive side, these threats to community wellbeing are countered to some extent through targeted measures carried out by the community association and its partnerships, and through beneficial governmental policy measures. Together, these responses help to reduce the detrimental effects of an unhealthy and dangerous environment, and of social concerns such as exclusion, poverty, urbanization and inequality. Key to the success of response measures are the contributions of the community leadership to improve the wellbeing of slum-dwellers by counterbalancing the effects of racism and social inequality, and implementing social programs and community facilities, thereby filling the gaps created by a lack of state support to slums. These actions illustrate what impoverished communities can do to improve livelihoods and wellbeing, and to combat problems such as environmental degradation and racial discrimination. This article also draws lessons for improving wellbeing analysis, particularly in slum communities, through a greater focus on (1) collective wellbeing and a community-focused view of wellbeing, (2) impacts of racism and inequality, and (3) interactions between community wellbeing and community leadership.
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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 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".