Aid donors, democracy and the developmental state in Ethiopia
Bibliographic record
Abstract
The “developmental state” has become a prominent alternative development model defended by contemporary Western aid donors, particularly in Africa. Purported “developmental states,” such as Ethiopia and Rwanda, are argued to possess strong-willed, visionary leaderships whose commitment to delivering on ambitious development plans renders them attractive donor partners. These leaderships are also, however, often authoritarian and unapologetic when criticized for democratic backsliding or human rights abuses. For many Western donors this represents a tolerable trade-off. The purpose of this article is to interrogate, critique and explain the assumptions and ideas underlying this trade-off. Using the case study of Ethiopia, we argue that donor officials’ understandings of “developmental state” are varied, vague and superficial, the main commonality being a “strong” regime with “political will” and a non-negotiable approach to domestic governance. We suggest that donors have too readily and uncritically accepted, internalized and deployed these notions, using the “developmental state” concept to justify their withdrawal from serious engagement on democratic reform. This derives from a systemic donor preference for depoliticized development models, as well as from Ethiopian officials’ own savvy political manoeuvrings. It has also, however, weakened donors’ position of influence at a time when the Ethiopian regime is debating major political reform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".