ASSESSMENT OF SOCIOLOGICAL DETERMINANTS OF WOMEN PARTICIPATION IN ELECTORAL AND POLITICAL ACTIVITIES: EVIDENCE FROM OKENE LGA OF KOGI STATE
Bibliographic record
Abstract
Research Problem: Despite global efforts to enhance gender parity, women’s political representation in Nigeria remains low, particularly in sub-national contexts like Kogi State. The 2019 elections in Okene LGA highlighted stark gender disparities, yet limited empirical studies have systematically examined the specific sociopolitical and structural factors constraining women’s political engagement in such local contexts. This study addresses the gap by interrogating the socio-institutional and attitudinal factors affecting women’s participation in Okene’s electoral process. Methods: The study employed a quantitative methodology, utilizing structured, close-ended questionnaires administered to respondents in Okene LGA. Descriptive statistics and logistic regression analyses were applied to evaluate patterns of perception and determine statistically significant predictors of female political participation. Theory:Anchored in Social Role Theory, the study assumes that persistent societal expectations shape political roles and reinforce normative gender hierarchies that influence women’s visibility and agency in the political space. Results:Findings revealed a contradictory reality: while domestic violence paradoxically increased political activism among some women, broader patterns of political violence, religious conservatism, gender-based leadership biases, low educational attainment, and prohibitive campaign costs severely deterred participation. Extremist threats and discriminatory norms compound the effect. Conclusion:Women in Okene face multi-layered deterrents to political involvement, necessitating both institutional reforms and sociocultural transformation. Key Contribution to Knowledge: This study nuances existing literature by quantifying and contextualizing women’s political deterrents within a localized Nigerian setting, emphasizing the contradictory impact of violence and the intersectionality of sociocultural constraints. Recommendations:It advocates legislative adoption of affirmative action such as gender quotas, increased civic education for women, and the subsidization of campaign costs to broaden political inclusivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".