Gambling in Sub-Saharan Africa: Traditional Forms and Emerging Technologies
Bibliographic record
Abstract
Purpose of Review: The gambling industry in Africa has seen substantial growth and evolution over recent years with a growing body of literature describing these shifts. Here, we provide a narrative synthesis of the extant literature on the origins, trends and consequences of the expansion and intensification of the commercial gambling industry in sub-Saharan Africa with a reference for future research on gambling as a growing public health concern. Recent Findings: The historical shift and permeation of gambling in sub-Saharan Africa is diverse with evidence of certain countries following a neo-colonial logic. Advances in technology have made gambling more accessible and created new markets in Africa. A key motive driving gambling on the continent is a lack of stable employment. While the intensification and growth of Africa's gambling industry has brought economic benefits to some African investors and individuals, this has been accompanied by a range of gambling harms. Legislation and policies designed to better regulate the gambling industry and redress these harms are needed. In this context, a small number of services and campaigns designed to mitigate gambling harms demonstrate promise, but more research is needed in this area. Summary: The gambling industry in sub-Saharan Africa has undergone a dramatic transformation. While it is true that the growth of the African gambling industry has provided an additional revenue stream to governments, it is also necessary to acknowledge the concurrent rise in gambling addiction and the health-related and social harms that it elicits. As such, designing effective regulatory measures and policy interventions that can reduce the public health burden of gambling harms is vital. However, these interventions need to take in to account the significance of cultural differences that exist among countries on the continent.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".