What Next for the Political Economy of Development in Africa? Facing Up to the Challenge of Economic Transformation
Bibliographic record
Abstract
Sub-Saharan Africa faces an alarming long-term outlook. With a massive demographic dividend in prospect, few countries have the means to turn this to their advantage by rapidly expanding employment-intensive economic sectors. Economic analysis is facing up to this challenge, with new attention to structural change, technology absorption and the capabilities of firms. However, this article argues, it has not got to the nub of the problem. Two connected issues have been under-examined: the productivity breakthrough in agriculture without which employment-intensive manufacturing will not take off; and the weak producer incentives generated by prevailing rural social-property relations. While most economists are ‘Smithian’ in their neglect of property relations, political science research has done less than it might to help. Responding energetically to ‘bringing the state back in’, it has generated a rich body of evidence on the configurations of power that make regimes effectively developmental. But these findings remain crucially incomplete. In future, the focus should be on the political economy of bringing productivity-enhancing social disciplines to the countryside.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".