The Productivity of Mistrust: Negotiating Partnerships in Ghana’s Development Sector
Bibliographic record
Abstract
Under the global Sustainable Development Goals (SDGs), partnerships have become central to the work of national non-governmental organisations (NGOs). Partnerships are underpinned by moral justifications of building trust and combating mistrust. A better understanding of how partnership policies are implemented in the everyday work of NGOs is critical for understanding the effects of moral imperatives in development ideologies as well as the current position and contributions of NGOs in the implementation of the SDGs. This dissertation examines how national NGOs in Ghana navigate the growing expectation that they build trust and how they affirm, negotiate, and contest partnerships. This ethnographic research draws on data from fifteen months of participant observation and in-depth interviews with a prominent network of NGOs working on the SDGs as well as the network’s government, private sector, and donor partners. As Ghana is considered a “development darling,” it offers an exemplary context for analysing the uptake of current aid policies. While trusting partnerships are framed in development ideologies as morally good, these policies occlude the uneven structures in which partnerships take place. I demonstrate that NGO leaders use practices and sentiments akin to mistrust to navigate the politics and hierarchies of partnerships in a development sector in which they have little decision-making power and are financially dependent on foreign donors. These operations of the development sector are revealed by paying attention to everyday practices of mistrust and emotional registers in partnerships, through which NGO leaders affirm their authority and recognition, even if they are unable to change the sector’s hierarchies. The ethnographic chapters on NGO relationships with government and private companies show how practices of mistrust constitute a moral positioning of integrity for NGOs as independent and working towards a social good. The chapters exploring partnerships within NGO networks and with donors demonstrate how the circulation of affective registers of suspicion and frustration develop common narratives of how partnerships should work, particularly in the top-down development context. Thus, this dissertation opens up to theoretical and empirical consideration the productive potentiality of mistrust and the surprising ways that practices and affects of mistrust constitute and sustain partnerships.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".