The Failure to Learn Lessons from Policy Failures in Developing Countries? The Case of Electricity Privatization in Ghana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Do policy makers learn from their failures? The rational and normal expectation would be that they do, but experience shows otherwise. Notwithstanding the valuable and multiply expected learning opportunities presented by such failures, especially in Africa, policy errors continue unabated in both the developed and developing worlds. Even the high-profile nature of the failures across the continent seems insufficient to convince African policy makers of their significance. Focusing on the recent electricity privatization fiasco in Ghana, this paper examines factors that impede or otherwise affect policy makers’ ability to learn from their mistakes. Using interviewing a number of officials involved in the process of electricity privatization, we identified five main factors that continue to affect policy learning from policy failures in Ghana.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it