The State of Policy Capacity Problems in Africa: The Viewpoint of Historical Institutionalism From Ghana
Bibliographic record
Abstract
ABSTRACT Why is policy capacity still a problem after years of capacity‐building initiatives in Africa? Many African states continue to lack policy capacity. Despite recognizing the limited policy capacity issues on the continent and all the capacity‐development initiatives that continue to take place, there has been a dearth of theorizing as to why this problem persists. How then can one explain the apparent dilemma? The failure to theorize policy capacity problems on the continent is extremely unfortunate. To explain this limited policy‐capacity problem, we turn our attention to historical institutionalism. Using Ghana as a case study, the paper attempts to demonstrate, from a historical institutional perspective, why capacity problems on the continent continue to exist. It is argued that the policy‐capacity problem stems from the inability of governments to bring about a seismic shift in thinking, distinct from that developed under colonial rule, with its over‐reliance on expatriates and consultants and path dependency. This paper constitutes desktop research and draws from both primary and secondary sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".