Ethiopia's developmental state: A building stability framework assessment
Bibliographic record
Abstract
Abstract Ethiopia has been lauded for its economic growth and progress in human development indicators. For some, that success is rooted in the developmental state approach advocated by the government. For others, the theory of the developmental state and the practice in Ethiopia were often at odds. Up until 2018, ideas that challenged the state and its approaches were not welcome, and politicians, academics and journalists were jailed for expressing alternative views. However, this appears to have changed, and in June 2018 the Deputy Prime Minister called for debate on the developmental state model. This article explores Ethiopia's developmental state model using the building stability framework, analysing its ability to establish fair power structures, foster inclusive economic growth, develop conflict‐resolution mechanisms, create effective and legitimate institutions, and enable a supportive regional environment. We find the developmental state was effective in a number of ways, but that this modality of governance appears to have passed its peak of securing advantage in Ethiopia. A shift from the developmental state to developmental democracy appears to be underway. Decision‐making and economic policies need to align with this change.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".