Bibliographic record
Abstract
This paper critically examines the impacts of Chinese-backed infrastructure projects in Uganda, focusing on the tensions and conflicts surrounding their development. It highlights how the anticipated benefits of these projects are often contested and met with collective resistance. Drawing on interdisciplinary frameworks of state capacity and contentious politics, we analyze two key examples of Chinese-backed infrastructure projects in Uganda: the Kampala-Entebbe Expressway (KEE) and the Entebbe International Airport Expansion Project. Our central argument is that Uganda’s weak state capacity facilitates poorly planned and executed projects, leading to their failure to meet expectations. Discontent arises from the gap between the promised futures of these infrastructure projects and the disappointing realities they have produced. This disconnect can be attributed to systemic issues stemming from the state’s inability to effectively govern and manage the projects. The consequences of Uganda’s weak state capacity include widespread corruption, a lack of transparency and accountability, disregard for human rights, inadequate risk assessment, and poor adherence to regulations. Furthermore, limited community engagement and the failure to address local concerns have compounded the socio-political challenges facing the country. These issues are reflected in the growing public disillusionment, as evidenced by the recurring protests against the government.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".