Fragile and Conflict-Affected States: Exploring the Relationship Between Governance, Instability and Violence
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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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".