General Elections 2013: A Case Study of Low Turnout of Women Voters in Khyber Pakhtunkhwa, Pakistan
Bibliographic record
Abstract
The Constitution of 1973 offers direct elections under adult franchise in Pakistan. However, it is unfortunate that in every election of Pakistan, the ratio of female voters’ turnout remained low. This research is an attempt to discuss the potential causes of low female voters' turnout in the province of Khyber Pakhtunkhwa. The study is based on quantitative data survey from six randomly selected districts from sub-geographical areas of Khyber Pakhtunkhwa Province in Pakistan including the districts of Lakki Marwat (Far South), Karak (South), Charsadda (Centre), Mardan (Centre), Lower Dir (North) and Chitral (Far North).The study utilizes Chi-square test for statistical inferences of dependent and independent variables. The research argues that the factors responsible for low turnout of women in these districts of Khyber Pakhtunkhwa can be categorized as administrative, cultural, political, and religious. The primary concern of respondents’ administrative obstacles were followed by cultural barrier and then by religious factors. Moreover, gender, the locality (district) and education of respondents cannot be ignored as they are key parameters as well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".