Bibliographic record
Abstract
“It is better to be President in a nondemocratic country than not to be President in a democratic one.” – Advisor to Frederick Chiluba, President of Zambia This chapter examines 14 competitive authoritarian regimes in Africa. The African cases share several features in common. First, they lacked favorable conditions for democracy. Nearly all of them were poor, rural, and had small middle classes and weak civil societies. Notwithstanding episodes of mass protest in some cases, large or sustained democracy movements were rare. Second, most African states faced an external environment characterized by high leverage and low linkage. Sub-Saharan African states were among the weakest and most dependent in the world, and the disappearance of competing Western security interests after the Cold War brought a sharp increase in external democratizing pressure. In 1990, the United States, United Kingdom, and France announced that future aid to Africa would be linked to democratic and human-rights performance. The new political conditionality had a major impact: The number of de jure single-party regimes in the region fell from 29 in 1989 to zero in 1994. At the same time, transitions took place in a context of low linkage. Africa's economic ties to the West were minimal, and information flows to and from the region were limited. In the early 1990s, Africa had the lowest density of telephone lines in the world, lacked virtually any internet connections, and was “severely underrepresented” in transnational human-rights networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".