Bibliographic record
Abstract
Throughout many of the first decades following independence, Africa's economies failed to grow; indeed in 2000 per capita incomes in several countries were lower than they had been in 1960. In this two-volume study, the African Economic Research Consortium (AERC) probes the nature and the roots of Africa's economic performance in the first decades of independence. We seek to describe Africa's growth experience in the latter decades of the twentieth century, to account for it, and to extract lessons to guide future policy-making in the continent. The timing of this two-volume assessment could not be more propitious. Debates over growth strategy have renewed as the region emerges from decades of economic decline and policy reform. Growth itself reignited in the mid-1990s, supported by policy reforms and also by rising commodity prices, a revival of aid flows, and the resolution of costly civil conflicts. What constitutes a pro-growth policy environment? What constrains the achievement of that environment? These questions were central to this examination of Africa's immediate past. The answers to them should feature in debates over how best to secure its economic future. We all recognize that the forces out of our control – the vagaries of commodity prices and climatic conditions, the rigors of fierce competition in fast-changing global markets, and the uncertainties of donor priorities and commitments – place limits on what we can attain. Even at the domestic level, important factors constrain our choices.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.387 | 0.308 |
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".