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Record W3163732299 · doi:10.3968/11625

Party Primaries and the Quest for Accountability in Governance in Nigeria

2021· article· en· W3163732299 on OpenAlexvenueno aff
Iwu Nnaoma Hyacinth

Bibliographic record

VenueCanadian social science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Sociopolitical Dynamics in Nigeria
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityDemocracyCorporate governancePoliticsPublic administrationPolitical scienceSelection (genetic algorithm)BiddingCivil societyPublic relationsSociologyLawEconomicsManagement

Abstract

fetched live from OpenAlex

Modern democracy highlights the importance of political parties both in agenda-setting and in displaying party aspirants from whom the electorates must choose. This paper examines the processes of candidate selection against the backdrop of demand for accountability from the political officeholders in Nigeria. Interestingly representative democracy builds on the theory that the citizens are in control of the process through which their representatives are elected but empirical evidence suggests diversities in the selection process. Nigeria has experienced about twenty-two years of uninterrupted democratic rule but each successive electoral period highlights a display of citizen’s discontentment with their representatives. This phenomenon raises a fundamental question about how their representatives were ab-ini-tio selected. There has been a paucity of research on how the conduct of party primaries set the contour for the people-oriented governance in Nigeria. This paper examines a candidate’s selection within parties and its implication for accountability. It argues that the structure of party primaries in Nigeria cannot but empower party bigwigs to impose aspirants that will undermine engendering accountability in governance. It argues for strong institutional mechanisms and civil society’s role to prevent elected representatives from doing the bidding of their godfathers.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.315
Teacher spread0.302 · 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.

Study designTheoretical or conceptual
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

Citations3
Published2021
Admission routes1
Has abstractyes

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