Capital Conversion in the Organized Crime of the Favelas
Bibliographic record
Abstract
Segregated in the hills of Rio de Janeiro, favelas are socially and economically marginalized slums, with pervasive drug crime. As a result of limited government intervention, drug lords assume the mandate in these sectors, reinforcing poverty and social exclusion.Traditional approaches to poverty analyze this context with capital scarcity as a point of reference. Moreover, the concept of capital has been used to denounce structural inequalities that are reproduced in social classes. By the same token, it is argued that the accumulation of capital may lead to social mobility. Low-income neighborhoods have their own resources and forms of mobilization. Conditions of precariousness can be explored without focusing on the absence of resources, but rather on the ways in which local capital gets mobilized and converted. In the favelas, drug gangs have their own capital dynamics that make them acquire and retain control over the territory. In this paper, I examine how capital conversion and mobilization among members of organized crime in these districts of Rio de Janeiro reinforce structural inequalities by perpetuating social exclusion.
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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 | 0.000 | 0.001 |
| 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 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".