African states, bureaucratic culture and computer fixes
Bibliographic record
Abstract
Abstract Our central argument in this article is that the introduction of computers in African states fails to produce the intended results. This is precisely because the trajectory of development of bureaucratic institutions in Africa has resulted in internal and external contexts that differ fundamentally from those of the Western states within which computing and information technology has been developed. This article explores the context in which computers were developed in Western industrialized societies to understand the circumstances that the technologies were designed to respond to and the bureaucratic culture that helped produce desired results. We then proceed to analyse the truncated nature of institution building in the colonial state, and how it structured the peculiar setting of the post‐colonial African state and dynamics surrounding the integration of the new information and communication technologies. We argue that the colonial state bequeathed to its post‐colonial successor three crucial characteristics that are of central importance to understanding why the introduction of computers does not produce anticipated improvements in public administration. These are the very limited technical capabilities of the bureaucracy; authoritarian decision‐making processes under the control of generalist administrators; and the predominance of patron–client relationships. Copyright © 2001 John Wiley & Sons. Ltd.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".