Structuring adaptations: Resilience, restrictive deterrence, and the Cunningham precursor control papers
Bibliographic record
Abstract
Inspired by Giommoni's assessment of the Cunningham precursor control scholarship, we propose two concepts to help drug policy scholars think through the mechanisms that operate when market participants are faced with a change in precursor availability. The first is the concept of restrictive deterrence, that emphasizes risks mitigation strategies such as looking into changes in the frequency, methods, markets that may occur after different types of interventions. While restrictive deterrence is an improvement over current approaches in thinking through adaptations, it falls short in its narrower focus on the individual, rather than organizations or the market as a collective. The concept of resilience is then proposed as alternative that allows scholars to elaborate specific hypotheses and assess both organizations and markets based on their capacity to anticipate, cope, adapt and ultimately recover from disruptions. We finish by providing a reading of the Cunningham and colleagues precursor control papers with the resilience framework in mind, showing that many of the elements were already present in their work.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".