Bibliographic record
Abstract
Natural resources, urbanisation, and population distribution create disparities in production and demand that drive a vast network of intraregional live animal trade in West Africa. It has long been argued that social capital is essential for long-distance transactions in a region where trade agreements are not fully executed and many barriers to trade exist. This paper examines the social and spatial structure of the trader networks that underpin regional trade. Using co-location social network analysis and 2013 to 2017 regional survey data from the Permanent Inter-State Committee for Drought Control in the Sahel (CILSS), it provides valuable insights into the interplay between economic factors and geographic constraints. The results reveal a fragmented and decentralised social network with border- and infrastructure-driven geographical fragmentation. They also suggest that the network relies on brokers who connect groups of traders with differentiated trade characteristics. The findings reinforce that, in the face of a regionally fragmented environment with many barriers, long-distance commodity flows rely on the social capital of traders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".