Fragile and Conflict-Affected States: Exploring the Relationship Between Governance, Instability and Violence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
‘Fragile and conflict-affected states’ (FCAS) constitute an increasingly important category of aid policy and action. But the category comprises a large and heterogeneous set of countries, problematizing coherent policy response which is often awkwardly split between boilerplate strategy and case-by-case approach. In both respects, efficiency of aid allocations is questionable. There is a need to disaggregate the category into smaller groups of countries, understood according to a more nuanced interpretation of the nature of their fragility. Disaggregation, however, is challenging insofar as it is hard to find a stable reference point internal to the category by which states’ relative performance – and causes of performance – can be determined. An alternative approach is to seek a reference point external to the entire FCAS category – for example a multilateral initiative – which allows us to explore systematic differences between those who sign up and those who do not. This research took the UN’s Scaling Up Nutrition (SUN) initiative as such a mechanism. Splitting FCAS into two groups – those who had joined SUN within its initial two-year phase and those who had not – we reviewed a range of social, economic, political, institutional and conflict/instability indicators to identify areas of significant difference. An unexpected finding was that while SUN-joiners performed statistically better on governance, there was no difference between joiners and non-joiners on the level of instability and violence they suffered, suggesting that some countries, even at high levels of conflict disruption, can achieve areas of relatively good governance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it