The New Electoral Marketplace and Voting: Conceptual and Empirical Insights
Bibliographic record
Abstract
This paper sets out to ascertain new electoral market for votes in the emerging democracies. It builds on earlier studies that tried howbeit in a limited way to explain the activities of political parties’ in African elections by extending the analysis to party identity, party branding and voting interface in Ghana. It interrogates the question: how does political brand identity influence voting behaviour? Drawing on in-depth interviews data and multiple strands of documentary analysis, it was established that political party identity and branding have little considerable influence on voting choices in Ghanaian elections. The results, show very little evidence to conceptually and empirically support the claim that party identity and branding have the propensity to influence voter choice from the perspective of the study participants. Based on the results, the paper concludes that voting behaviour in Ghana vary with policy choices, and more associated with mounting electoral-economic disequilibrium, given socio-economic constraints such as fluctuations in the market price of goods and services. Therefore, political parties should adopt appropriate policies and strategies, producing electorally competitive electoral market. This will seemingly produce a party identity, branding and voting during elections with the demand and supply voters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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.
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".