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Record W2964700366 · doi:10.31014/aior.1991.02.03.100

Designing E-Voting As An ‘Apparatus’ For Combating Election Rigging: A Nigerian Model

2019· article· en· W2964700366 on OpenAlexaboutno aff
John Sunday Ojo, Godwin Ihemeje

Bibliographic record

VenueJournal of Social and Political Sciences · 2019
Typearticle
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsnot available
Fundersnot available
KeywordsVotingDemocracyRanked voting systemPolitical scienceLanguage changeLeagueBusinessEconomicsLaw

Abstract

fetched live from OpenAlex

This is a proposed Nigerian model of e-voting. The lessons learnt and huge successes recorded from countries that have practiced e-voting system such as Belgium, Brazil, Canada, Estonia, France Germany, India, Ireland, Netherlands, Norway, Switzerland, United Kingdom and the United States of America beaconed hope for adopting E-voting system as capable of proffering solution to electoral rigging in emerging Nigerian democracy. This paper concludes that while electronic voting is not a magic wand, it is the surest way yet for Nigeria to join the league of countries that have wiped out electoral fraud, which is the worst form of corruption. Concurrently, it is also the best way to hand over for the incoming regimes.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.032
GPT teacher head0.316
Teacher spread0.284 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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