Factors That Militate Against Women Participation in Politics in Enugu State
Bibliographic record
Abstract
Abstract This research studied factors militating against women’s participation in politics in Enugu state using four communities (Amokwe, Ikpamodo, Ndeaboh and Eha-Amufu) drawn from three senatorial zones as case study sites. The research approach employed was Community Familiarization Visits, Focus Group Discussion (FGD) and In-Depth Interview (IDI). The FGD comprised of women and men leaders in the selected communities who served as the respondents while in the case of IDI the selected women community leaders served as the respondents. The study revealed among other things that women from the study areas have not been actively involved in politics since the return of Democracy in 1999 mainly due to poor finance, lack of education, lack of support from fellow women and an unfavorable political environment. The study thereby recommends different measures to end women’s discrimination and intimidation, and the creation of a favorable environment for the improvement of women’s political participation in Enugu state.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".