Linkages Between Large-scale Infrastructure Development and Conflict Dynamics in East Africa
Bibliographic record
Abstract
With the rapid increase in the number of mega-infrastructure projects underway across East Africa, how the social, economic, political and environmental repercussions of these projects intersect with ongoing conflict dynamics is a poorly understood topic. Although recent interest in large-scale land acquisitions has led to a number of detailed investigations into specific projects and trends, there has not yet been a broad, systematic review of how large-scale infrastructure developments in East Africa interact with previous, ongoing and potential conflict in their areas of operation. The objective of this article is to report on an analysis of 26 mega-infrastructure projects across Kenya, Tanzania, Ethiopia and Uganda, with an explicit focus on the common tension points that contribute to security dynamics. The methodology used involved two composite indicators of risk—a conflict risk score and a project impact score. The study found seven common tensions across all projects: in-migration, population displacement and relocation, a negative history of community relations with previous or follow-on developments, land rights, securitisation, environmental degradation and expectations of the local population relative to benefits delivered by the project. The study recommends increased attention on prior assessments that focus on the broader and more interconnected impacts in addition to those confined to the immediate project location, as well as in-depth examination of possible mitigation measures. JEL Classification: O1, O2, Q2, Q3, Q4, R1, R4
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".