Rethinking organized crime in Africa
Bibliographic record
Abstract
Abstract Much of the existing research on organized crime in Africa has emphasised its development and proliferation from state and security perspectives. Such research often relies upon inflated facts for captivating public attention, is fuelled by sensationalist media reports and draws from conceptualisations that give an incomplete picture of the significance of illicit activities, both for the state and their role in enabling and sustaining people’s livelihoods. In contrast, this special issue proposes that more empirical research and analysis is needed to reveal the disjunctures between state and on-the-ground perceptions. Greater attention to a bottom-up vision of illicit activities can demonstrate how defining and understanding these practices through such binary terms as legal/illegal does not necessarily indicate how those engaged in them perceive them. Through bringing together a range of contributions from different disciplinary, theoretical and empirical perspectives, this special issue explores the space between official, policy-driven narratives of crime and the realities of the everyday nature of these practices, in a bid to rethink and challenge the ‘organized crime’ lens through which these activities are increasingly framed.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".