Capacity development for education service delivery in Pakistan: Top‐down devolution
Bibliographic record
Abstract
Abstract The historical, political, socio‐cultural and institutional context in the public service of Pakistan is not auspicious for the delivery of social services such as education. The then military regime introduced radical devolution reforms in 2001 that promised improvements in service delivery by enhancing accountabilities and capacities for change in local government. However the political economy of this top‐down devolution has proved contentious. It established new power structures and authorities over resources at local levels but without concurrent efforts to enhance service delivery capacities. This article examines capacity issues in two cases of capacity development in education service delivery in Pakistan's largest province. The Punjab Education Sector Reform Programme (PESRP) was managed by a provincial‐level implementation unit; the Strategic Policy Unit (SPU) of City District Government Faisalabad was a local government project supported by technical co‐operation. Both delivered major improvements in education delivery capacity in just 4 years, after decades of delivery stagnation and worsening education indicators. The sustainability of these initiatives is in doubt, as political economy factors remain a major impediment to devolved service delivery in Pakistan. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".