Fronts and Friends: Social Contingencies in the Management of Drug Debt
Bibliographic record
Abstract
Illicit drug markets have long been associated with violence as a mode of regulating market behavior, especially regarding debts linked to drug purchase. While a growing literature examines violent and nonviolent modes of ensuring repayment by dealers and lenders, little research has focused on strategies of buyers and borrowers in navigating drug debt. Drawing on interviews with 75 people who use drugs within a materially disadvantaged neighborhood, we explore experiences in managing debt to dealers and within social networks adjacent to drug markets. Findings describe complex strategies to protect reputation, foster relationships with dealers, and employ cooperative, assertive, or coercive tactics to negotiate credit arrangements that sustain and stabilize the drug market while mitigating violent retaliation for unpaid debt. Findings also elucidate informal credit arrangements within social networks, identifying reciprocity and self-control as constitutive of social capital within friendship groups that serve as financial and social safety nets. This research considers socially embedded, boundedly rational decisions of marginalized drug market actors, highlighting diverse financial management practices among structurally vulnerable borrowers that serve economic and social goals while seeking to mitigate the risk of violence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".