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Record W4402918635 · doi:10.6000/1929-7092.2024.13.02

What is the Cost? The Effect of the Post-Independence Government on Public Sector Performance in Namibia

2024· article· en· W4402918635 on OpenAlexvenueno aff
Christopher P.P. Shafuda, Utpal Kumar De

Bibliographic record

VenueJournal of Reviews on Global Economics · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsIndependence (probability theory)Public sectorGovernment (linguistics)BusinessPublic economicsDevelopment economicsEconomicsEconomyMathematics

Abstract

fetched live from OpenAlex

The end of colonisation in Sub-Saharan Africa did not bring the expected welfare to many. Several governments stumbled with public resource mismanagement, which created room for corruption and other malpractices (Good, 1994: Maipose, 2000: Ncholo, 2000: Tsatsu, 2015). This article examines the performance and efficiency of the Namibian government interventions between 1990 and 2020. The findings reveal that some indicators showed significant improvement, while some did not record any substantial improvement. Decent performance was recorded in government policies to reduce inequality and poverty. Good performance is also seen in policies improving education attainment, while policies to reduce unemployment performed poorly. Other indicators have shown mixed results. The results also revealed the worst performance during the first five years after independence. The period between 2016-2020 recorded poor government performance in most of the indicators. The findings suggest a need to improve performance and efficiency in the public sector.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.262
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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