Bibliographic record
Abstract
This dissertation examines the demand for military spending in Sub-Saharan Africa. It comprises three chapters. The first chapter uses a qualitative method, including key informant interviews, to examine four important issues related to Uganda's defence budget: the process leading to its formulation, the key actors involved in that process, the structure of military expenditures, and the internal and external threat environment. The analysis in that chapter shows that defence spending in Uganda generally follows a process of intra-governmental bargaining and political oversight over public expenditures. Despite the government's military roots, and the President's ultimate control over military spending, the defence budget has not overwhelmed other government priorities. In addition, the nature and level of internal and external threats to Uganda do not seem to pose a serious challenge to its security. While there are Chapter three uses panel data to examine military spending in a sample of 30 of Sub-Sahara African countries for the period of 1988-2016, the largest sample for which data are available. Two distinct specifications are performed, a fixed effects model and a dynamic panel data model. The results of different regressions performed in this chapter show that the size of the economy, demography, changes in the level of military spending of neighbouring countries, and the lagged of military spending are the most important explanatory variables of the demand for military spending in Sub-Saharan Africa. Furthermore, the estimations of the fixed effect models show that the nature of the political regime (autocracy and democracy) and the occurrence of civil war also influence the defence budget. However, the estimations of the dynamic panel models fail to corroborate this influence. Finally, post-estimation tests show that both the fixed effect and dynamic panel data models are suitable and efficient for this analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".