Bibliographic record
Abstract
The solution to the fragility and instability that accompanied Africa's newly independent states was widely seen as the adoption of strong, unrivalled political leadership. Politics needed to be uncompetitive; there was to be none of the jockeying for power between rival parties witnessed in older, more well-established democracies. This path, however, soon proved disastrous. Time and time again, the absence of a viable political opposition allowed leaders to run roughshod over the public good. Real incomes fell as a tiny kleptocratic elite enriched themselves at the national expense. Since 1989, however, Africa has witnessed a remarkable liberalization of political competition. Opposition parties have in most places been granted newfound freedom to politick, and have on occasion won elections outright. Some argue this is at least in part the product of the diffusion of communications technology. They contend such devices facilitate political activism and organization, fostering political competition in its wake. To test this hypothesis, continent-wide data on the spread of landline telephones, wireless handsets, and Internet connections has been marshalled, then contrasted against the level of political competition in Africa throughout the post-colonial period. The findings in this chapter suggest that while communications technology may serve as a boon to the cause of political competition immediately following first introduction, its long-term effects are likely to be limited.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.167 | 0.082 |
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".