Service Delivery in South Africa
Bibliographic record
Abstract
Africa (UNECA). This paper was presented at the workshop on “Public expenditure and service delivery in Africa: Managing public expenditure to improve service quality and access ” organized by ECA from 11-13 October 2006, Lusaka, Zambia. Comments from participants at the workshop and my colleague Amal Elbeshbishi are gratefully acknowledged. ATPC is a project of the Economic Commission for Africa with financial support of the Canada Fund for Africa This publication was produced with the support of the United Nations Development Programme (UNDP). Material from this publication may be freely quoted or reprinted. Acknowledgement is requested, together with a copy of the publication The views expressed are those of its authors and do not necessarily reflect those of the United Nations. This paper uses provincial level data from South Africa to examine how fiscal decentralization impacts basic service delivery, focusing on the role of own-source revenue. Theory suggests that fiscal decentralization and particularly revenue autonomy as represented by own-source revenue enhances service delivery through increased accountability and transparency of policy makers and service providers as well as increased responsiveness to local preferences and needs. The South African federal system is characterized by a relatively high degree of fiscal decentralization in terms of expenditure responsibilities and administration. However, owing to acute historical imbalances across provinces and municipalities,
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".