Informality, Illegality, and Criminality in Mexico's Border Communities
Bibliographic record
Abstract
Intellectual property rights groups and formal industry associations have long argued that the avoidance of taxes and regulations and the increase in the sale of counterfeit, contraband, pirated, and stolen goods in countries like Mexico contribute to a climate of illegality that incentivizes crime and benefits organized crime. Such informality could play a contributing factor to explain the rise and persistence of violence in Mexico. This concern is particularly relevant in border regions where organized crime controls illicit transnational flows and where many counterfeit and contraband goods cross national boundaries. This paper asks to what extent the informal sector facilitates illegality and criminality more broadly and explores three potential hypotheses: (1) organized crime might find a business opportunity in mitigating the opportunity costs to operating outside the formal economy; (2) intermediaries who manage the state's contradictory strategy of enforcement and tolerance might exploit their structural positions for private gains; and (3) criminals and organized crime might benefit from a climate of illegality. Through an exploration of the informal market for used cars illegally imported from the U.S. and the pirated and counterfeited goods business in Mexico's northern border communities, I find only limited support for the first hypothesis but considerable evidence for the second and third.
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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 | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".