Resource Complementarity for Social Service Delivery: A Case of Corporates and Local Government Authorities in Morogoro, Tanzania
Bibliographic record
Abstract
Employing a case study design, this paper examined corporates’ resource complementarity to Local Government Authorities in Morogoro region. The findings revealed that as much as corporates such as Kilombero and Mtibwa Sugar Companies comit significant amount of resources through CSR to offer social services, which could otherwise be offered by the LGAs; the LGAs are less informed of the actual contribution made by corporates in terms of the actual budgets spent to complement social services in their areas of jurisdiction. The findings further revealed that poor flow of information from Villages and Wards levels to the District Council has accounted for this challenge, which has resulted into LGAs failing to acknowledge contribution made by the corporates. Failure of LGAs to acknowledge the contribution of Corporates has far reaching impact on the reputation of the corporates and the motivation of corporates to sustain resource complementarity to LGAs. This paper recommends that, there is a need to improve information system within LGAs on the resources complemented by corporates and other stakeholders for social services delivery. Such improvement should encompass production and submission of reports on resources and social services provided through CSRs by development stakeholders to the District Councils.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".