Fragile Statehood and Military Aid (In)Effectiveness: An Assessment of United States Experiences in Lake Chad Basin
Bibliographic record
Abstract
This dissertation critically examines the (in) effectiveness of US. military aid in fragile states, with a particular focus on the Lake Chad Basin region.Through a detailed investigation of the political, economic, and social impacts of US. military assistance, the research uncovers both the intended and unintended consequences of such aid on state stability, governance, and human security.Central to the argument is that increased US. military aid to countries with weak governance structures often exacerbates instability, fosters corruption, and leads to human rights violations.The study demonstrates that, in the absence of parallel improvements in governance and state legitimacy, such aid contributes to the very conflicts it seeks to resolve, trapping recipient states in a cycle of instability.The dissertation is structured into three interconnected papers, each examining different dimensions of US. military aid in the region, employing both quantitative and qualitative methods to assess its role in shaping political outcomes, security landscapes, and democratic trajectories.Through a comprehensive analysis, it reveals the convergence of a negative feedback loop, capacity trap, democratic backsliding, and a troubling rise in coup d'états.Moreover, US. military assistance has focused on short-term security gains at the expense of longterm investments in governance, development, and institutional reform, deepening fragility and conflict in the region.Ultimately, the study concludes that US. military aid, while intended to stabilize fragile states like those in the Lake Chad Basin, often exacerbates governance challenges, highlighting the urgent need for a more integrated approach that prioritizes sustainable governance and human security over purely military solutions.
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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 | 0.002 | 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 | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".