Effects of institutional capacity, infrastructure governance, and equity on state- and nation-building processes in Ethiopia
Bibliographic record
Abstract
This paper analyzed the equitable allocation of infrastructure across regional states in Ethiopia. In general, in the past years, there has been a good start in the infrastructure sector in Ethiopia. However, the governance and equity system of infrastructure in Ethiopia is not flexible, not technology-oriented, not fair, and not easily solved. The results of in-depth interviews and focus group discussions (FGDs) showed that there is a lack of institutional capacity, infrastructure governance, and equity, which has negatively impacted the state- and nation-building processes in Ethiopia. According to the interviewees, so long as the unmet demand for infrastructure exists, it remains a key restrain on doing business in most Ethiopian regional states. This is due to the lack of integrated frameworks, as there are coordination failures (lack of proper government intervention, including a lack of proper understanding and implementation of the constitution and the federal system). In Ethiopia, to reduce these bottlenecks arising from the lack of institutional capacity, infrastructure governance, and equity and their effects on nation-building, first of all, the government has to critically hear the people, deeply assess the problems, and come to the point and then discuss the problems and the way forward with the society at large.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| 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".