Criminalized crops: Environmentally-justified illicit crop interventions and the cyclical marginalization of smallholders
Bibliographic record
Abstract
Despite decades of efforts to curb the global supply of illicit drugs and significant shifts in how those efforts are designed and implemented, illicit crop cultivation persists. In this paper, we examine state and international development efforts to eradicate coca in Peru, opium poppies in Laos, and cannabis in California, USA and the ever-changing discourses used to justify and design these interventions. Scholarship in political geography frames eradication interventions as serving ongoing efforts to extend state and market power into the regions in which illicit crops are grown and to marginalize the people growing them. We find that environmental discourses are increasingly used to assert the need for continued illicit crop interventions, and that these discourses articulate with historical and ongoing portrayals of smallholders as environmentally destructive. Environmental harm narratives that justify enforcement and eradication efforts under the guise of protecting ecosystems from illicit crop farmers can become self-fulfilling prophecies when they disproportionately impact smallholders and push them into marginal geographic and economic positions. Our cases illustrate that environmentally-justified interventions drive cycles of marginalization for illicit crop smallholders, often conditioned by race or ethnicity, who are then portrayed as environmental criminals. Meanwhile, new state-sanctioned spaces of opportunity and profit are created for more powerful actors who are able to capitalize on the removal of illicit crop growers from the land.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".