The Baltic and Nordic responses to the first Taliban poppy ban: Implications for Europe & synthetic opioids today
Bibliographic record
Abstract
The 2000-2001 and the 2022-2023 Taliban opium bans were and could be two of the largest ever disruptions to a major illegal drug market. To help understand potential implications of the current ban for Europe, this paper analyzes how opioid markets in seven Baltic and Nordic countries responded to the earlier ban, using literature review, key informant interviews, and secondary data analysis. The seven nations' markets responded in diverse ways, including rebounding with the same drug (heroin in Norway), substitution to a more potent opioid (fentanyl replacing heroin in Estonia), and substitution to one with lower risk of overdose (buprenorphine replacing heroin in Finland). The responses were not instantaneous, but rather evolved, sometimes over several years. This variety suggests that it can be hard to predict how drug markets will respond to disruptions, but two extreme views can be challenged. It would be naive to imagine that drug markets will not adapt to shocks, but also unduly nihilistic to presume that they will always just bounce back with no lasting effects. Substitution to another way of meeting demand is possible, but that does not always negate fully the benefits of disrupting the original market. Nonetheless, there is historical precedent for a European country's opioid market switching to synthetic opioids when heroin supplies were disrupted. Given how much that switch has increased overdose rates in Canada and the United States, that is a serious concern for Europe at present. A period of reduced opioid supply may be a particularly propitious time to expand treatment services (as Norway did in the early 2000s).
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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 | 0.004 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".