Exploring the complexity of partnerships in development policy and practice: Upstairs and downstairs
Bibliographic record
Abstract
Abstract Motivation The term partnership, and the balance of ownership within it, significantly influence the direction of the development field and whether it will be able to address increasingly complicated global challenges such as climate change, peace and security and growing inequality. Purpose The article explores the nature of government donor–recipient partnerships, the struggle over ownership, and the possibility of transitioning from top‐down aid policy to genuine development co‐operation. Approach and Methods The discussion is based on the lead author’s doctoral research and the authors’ experiences of working with the Coady International Institute and the Roméo Dallaire Child Soldiers Initiative. Findings The research revealed ample evidence that proclamations of more equitable partnerships or recipient ownership of aid policy are undermined by historical power dynamics and coherency to dominant development narratives. However, a closer examination also found some room to create change as policy is negotiated and interpreted in a multitude of smaller policy spaces, including influences from networks of civil society organizations (CSOs). The article looks at two CSOs that use their “downstairs” position to act as interlocutors with Southern partners. In some cases, they fostered more equitable partnerships and support South–South networks by applying an emancipatory learning approach and adapting aid modalities. This points to the potential for slow—and often reluctant—progress towards more equitable global partnerships and innovative practices. Policy Implications The findings suggest that the asymmetrical nature of government donor–recipient partnerships can be addressed through a more nuanced learning approach and increased engagement with CSOs that can experiment with project modalities and support for CSO networks.
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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 | 0.055 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.080 |
| Scholarly communication | 0.031 | 0.034 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.005 | 0.008 |
| 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".