Participatory rural development program and local culture: a case study of Mardan, Pakistan
Bibliographic record
Abstract
Research on rural development has gained significance in recent years.Much of the previous work focused on economic factors of the people involved in rural development programs/projects.However, now there is a growing interest in the role of socio-cultural factors affecting rural development.This paper reviews and analyzes how a German assisted Integrated Rural Development Program (IRDP) effectively and efficiently incorporated the people of a diverse culture of Mardan Division of North West Frontier Province, Pakistan in the development activities through a process oriented socio-cultural approach.The new way of viable participatory institution building at meso level (Regional Development Organizations) to tie and pursue the interest of micro-level organizations (Community Based Organizations) based on traditional, cultural values, and norms indicates how participation can be effectively institutionalized and the continuity of collective actions is ensured in the shape of Integrated Rural Support Program even after the withdrawal of IRDP.Based on the interviews, discussions, observations, and references, it is evident that the IRDP participatory approach not only facilitates genuine participation of the people in problem solving, planning, institutional building, and implementation of development activities but also ensures the sustainability of grass root organizations in the long run.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".