Political Economy Analysis of Voter Participation and Choices in National Elections in Ghana’s Fourth Republican Era
Bibliographic record
Abstract
We analysed the determinants of voter participation (turnout), impairment of voter participation (spoiled or rejected ballots), and the outcomes (share of the total valid votes cast garnered by the victorious political party) in national presidential elections during the Fourth Republican era in Ghana. This analysis was undertaken based on meso-level statistical models, using district-level data of voters compiled from constituency-level data maintained by the Electoral Commission of Ghana, and district-level socio-economic characteristics derived from the 2010 and 2000 National Population Censuses conducted by the Ghana Statistical Service. In essence, we used data from two presidential elections in Ghana in 2000 and 2012 which could be directly aligned to data from the 2000 and 2010 national population censuses for district-level analysis using the concept of an average “district” voter. Our analysis indicated that the voter turnout was determined by a number of factors, the most important one being the population aged 15 over; the turnout decreases with increasing population. The impairment of voter participation, based on the proportion of the total votes cast attributed to spoiled ballots, was linked to the literacy rate with the spoiled ballots proportion declining with increasing literacy rate. The share of the total valid votes cast, obtained by the victorious party in a district, was influenced to a large degree by the proportion of the total number of citizens in a district belonging to the two biggest social/ethnic groups in Ghana, Asantes and Ewes, who predominantly voted in a countervailing manner for the parties that their political class elites dominate, the New Patriotic Party and National Democratic Congress, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".