The Effect of SMS Service on the Political Participation of Person with Disabilities (PWDs) in Khyber Pakhtunkhwa (Pakistan)
Bibliographic record
Abstract
Persons with disabilities have been remained socially excluded in developing societies. The situation is not quite different in Pakistan, particularly in Khyber Pakhtunkhwa (The Province of Pakistan) as there is no special arrangement for civic participation especially in elections. The following study is pioneering effort in order to understand their level of participation in political process. In addition to this, this study also explores the role of SMS service in enhancing political participation of persons with disabilities. The study was conducted in four districts, Mardan, Swat, Swabi and Malakand. Four hundred and three persons with disabilities were surveyed during this base line study. Findings showed majority of the persons with disabilities did not cast their vote in 2008 general elections due to unavailability of the special arrangements for the disabled persons. For 2013 general elections, the greater proportion of persons with disabilities intended to cast their vote because most of the respondents know about the newly launched short messaging services (SMS) of the Election Commission of Pakistan and most of the respondents checked about registration number and location of the polling station by sending the SMS to 8300. The study confirms a positive association between knowledge about SMS service and voting attitude of person with disabilities, indicating that technology can be utilized in enhancing political participation of person with disabilities.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".