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Record W2981775111 · doi:10.22215/etd/2018-13262

The Demand for Military Spending in Sub-Saharan Africa

2018· dissertation· en· W2981775111 on OpenAlexaff
Decky Kipuka Kabongi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsGovernment (linguistics)Government spendingDevelopment economicsPoliticsInternal securityPublic economicsEconomicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

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 several perceived threats that may influence Uganda's defence budget, the limited volatility of

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.046
GPT teacher head0.266
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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