The politics of establishing pro-poor accountability: What can poverty reduction strategies achieve?
Bibliographic record
Abstract
The Poverty Reduction Strategy (PRS) experiment, along with other innovations promoted by the international financial institutions over the past decade, has promised to secure pro-poor forms of accountability in relation to development policy-making. New consultative processes and new forms of conditionality each promise to re-order relationships between poor citizens and their governments, and between governments and donors respectively. Using evidence from Bolivia and Zambia, we identify three critical problems with these claims. First, there is a tendency to focus on promoting accountability mechanisms that are largely discretionary and lack significant disciplinary power, particularly those reliant on certain forms of civil society participation. Second, donors have failed to overcome the contradictions regarding the role of extra-national actors in securing accountability mechanisms within particular states. Third, there is a tendency within the PRS experiment to overlook the deeper forms of politics that might underpin effective accountability mechanisms in developing countries. Ensuring accountability is not simply a technocratic project, but rather is critical for a substantive politics of democratization which goes to the heart of the wider contract between states and citizens. The PRS experiment, as located within a broader project of ‘inclusive liberalism’, reveals little potential to address this challenge.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 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".