Exploring hidden narratives in the West African Tramadol trade and transport of migrants
Bibliographic record
Abstract
West Africa’s role in illicit flows and their control has become a central concern for international and domestic policy makers of late, with attention coalescing around threats such as drug trafficking and irregular migration. Our current understanding of these activities and their alleged links to crime relies mostly on depictions from the outside, as West African data and narratives have been largely ignored. These narratives, however, can help to better understand what these activities mean locally, what has caused their emergence and what can address them as a policy issue. The Hidden Narratives on Transnational Organised Crime in West Africa project explores the narratives on two activities with a particularly ambiguous legal status, which have increasingly been linked to organised crime: the trade in Tramadol (a synthetic opioid) and the transportation of migrants. The project uses in-depth interviews with individuals involved in these activities and their regulation, focusing on two regional trade and control centres: the port city Lagos (Nigeria) and trans-Saharan hub of Agadez (Niger). As part of the project, a policy workshop was held in Ibadan (Nigeria) on 9 and 10 March 2020. It engaged key policy makers and practitioners from national, international and non-government agencies with research on the trade in Tramadol and the transport of migrants in Nigeria and Niger. The workshop discussions form the basis of this policy brief.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".