Gangs as states and firms: Criminal organisations, smuggling markets and violence on Brazil’s frontiers.
Bibliographic record
Abstract
Large criminal groups are often thought of as distinctive entities with their own rationales, specialised governance arrangements, and an inherent tendency to use indiscriminate violence.Arguably, criminal organisations instead behave very much like states and legal firms.They behave like states in their preoccupation with borders, dedicating attention and investment to some areas while not others, and they behave like firms by expanding to capture market share and vertically integrating in response to changes in their market environment.They make decisions based on locale-specific and market-specific costbenefit assessments and on the conditions imposed on them by exogenous shocks.In this dissertation, I argue that the frequently referenced "border effect" on criminal violence does not hold in Brazil, the country with the longest border in the whole American continent.Instead, hotspots central to organised criminal operations drive overall border violence.In addition, criminal governance takeovers can be understood as the product of a vertical integration, a process that, given the local context, can made perfect "corporate" sense.Also, I consider the internal logic of the so-called "balloon" and "cockroach" effects, showing them to be more than simple metaphors to describe the ineffectiveness of anti-crime initiatives.These effects can be instigated by states but also criminal groups and combine into chain reactions whereby the original shocks that trigger them lead to unintended consequences.Based on these results, I identify significant linkages that emerge between border hot spots, vertical integration by criminal organisations, and the knock-on effects that result from exogenous shocks to criminal organisations' operations.I call for a more pragmatic approach to examining criminal organisations: instead of thinking of them as distinctive entities with characteristics that set them apart from others, they behave to a large extent like "rational" firms and states that make informed decisions based on their local environments and the iii resources at hand.The importance of understanding the local-level conditions of criminal markets cannot be understated as they are crucial to identify the capacity or incentives of groups to effectively govern their competition with competing claimants to resources which, when lacking, often leads to violence.First and foremost, I would like to thank my supervisor, Jean Daudelin, who has been steadfast in his support for me throughout this dissertation.Thank you for your time, guidance, and especially your patience through the good and the bad.Without you I would not have discovered the world that is Brazil's borderlands.I am truly grateful.I also want to thank Dane Rowlands and John Sidel, my two
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".