Coordinating cross-sector partnerships for water provision in Malawi
Bibliographic record
Abstract
Subject area Public sector management, stakeholder management, collaboration and strategy. Study level/applicability Undergraduate (final-year) or master's-level students (Master in Public Administration, Master in Management). Designed for courses in nonprofit management, public administration and/or international development. Can also be used in any course, such as strategic management, sustainable development or corporate social responsibility, that covers stakeholder theory, or stakeholder management as a topic. Case overview Decentralization has changed the way core services are delivered to local populations in sub-Saharan Africa. This in turn has forced nongovernmental organizations, international aid agencies, corporations and other development partners to change the way they engage with government in their shared efforts to help improve the living conditions of people living under the threshold of poverty in this and other parts of the world. This modular ethnographic teaching case uses the specific example of the water sector in Malawi to help highlight the complexity of multiple stakeholder relations in an international development context. Expected learning outcomes Upon completion of this case, students should be able to: identify and understand the different goals and issues that individual stakeholders in cross-sector partnerships are dealing with; identify and understand the power/control dynamics at play in these relationships; analyse the advantages and disadvantages associated with different ways of coordinating multi-stakeholder partnerships; and develop recommendations for structuring multi-stakeholder relationships in developing and emerging markets that balance efficient service provision with concern for individual stakeholder priorities. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".