Evaluating Multi-Sector Partnerships for Sustainable Community Development in Nigeria
Bibliographic record
Abstract
More than ever, multi-sector partnerships are being seen as a key community development approach, with many governments, corporate bodies, and international agencies viewing them as an effective way of addressing complex development challenges that have defied single-sector interventions. In Nigeria, corporate bodies have, before now, demonstrated their commitments towards community development directly and independently. But presently, the attention has shifted to partnership approach for sustainable community development. The aim of this paper is to have an insight on the multi-sector partnerships employed by Shell Petroleum Developing Company – the Nigerian subsidiary of Royal Dutch Shell, aimed at poverty reduction, and sustainable community development in their host communities. The paper uses a qualitative approach through exploring of relevant secondary sources and content analysis to evaluate the company’s partnership initiatives. It argues that such partnerships have impacted more positively on the people by empowering community members, enhancing community well being, and solving community problems than the company’s previous approaches to community development. Key words: Partnership; Poverty Reduction; Sustainable Community Development; Oil Multinational Companies; Bottom-up
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".