The Causes and Measurement of State Fragility
Bibliographic record
Abstract
This chapter is derived from our ongoing research on fragile states funded by the Canadian International Development Agency (CIDA) to help policy-makers and analysts make decisions on where and how to allocate aid, especially in fragile state environments. In order for development assistance to have a measurable and positive impact on fragile states, it is necessary to understand both how and why they become fragile. First, we reconceptualize the meaning of state fragility with equal attention given to the authority, legitimacy and capacity of a state, collectively referred to as authority, legitimacy, and capacity (ALC). Measures of these ALC components corresponding to six different categories of state performance—economics, governance, security and crime, human development, demographics, and the environment—are collected for all countries for the period 1999-2005. Initial testing of our fragility index shows that fragility is driven by a number of factors, of which the level of development seems to be more important. We complement this analysis by examining state fragility using the ALC framework. Overall, the approach presented has the distinct advantage of identifying country-specific patterns of fragility while at the same time allowing for broad strategically relevant measures of comparative performance that can be of use to policy-makers regarding allocation of aid at the sectoral and programming level. Notwithstanding the fact that aid may be allocated for political and strategic reasons, and that fragile states are under funded, we argue that aid that does flow to fragile states could be better targeted. Specifically, it could strengthen the underlying determinants of fragility by addressing fragile states’ distinct and country-specific weaknesses in authority, legitimacy and capacity. Finally, we discuss policy implications of our analysis and directions for future research.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".