From Fragility to Stability: A Novel Transition Model for Third-Party Interventions in Fragile and Conflict Affected States
Bibliographic record
Abstract
Policymakers and scholars alike have wrestled with the question of why some third-party interventions in fragile and conflict affected states (FCAS) are successful while others are not. For example, countries such as Cambodia, Rwanda, and Uzbekistan have been pulled out of extreme fragility, whereas countries such as Libya, Mali, and Zimbabwe remain mired in extreme fragility despite numerous attempts at spurring economic, social, and political development. This dissertation examines post-intervention variations in the development and security outcomes in otherwise similar fragile countries, in order to explain why some Western interventions have failed while others have succeeded. By documenting key cases of third-party intervention in FCAS and examining them systematically this research program tests structural aspects of each country situation while at the same time accounting for agency and leadership roles, both domestically and internationally. Using a theoretical framework based on the evolutionary biology principles of alternative stable states and positive feedback loops, the findings indicate that there is some evidence that—perhaps counter intuitively—outside interventions should focus initially not on the weakest state fragility dimension (authority, legitimacy, and capacity) but rather on the strongest. For states stuck in extreme fragility for long periods of time, the level of each of the three dimensions is so low that focusing on the weakest dimension only leads to premature load bearing and isomorphic mimicry problems (Andrews, Pritchett and Woolcock 2017). Rather, interventions which target the strongest dimension of fragility lead to improvement by establishing self-reinforcing, 'runaway' positive feedback loops. At the same time, the findings indicate that intervention on the strongest dimension may be a necessary but not sufficient factor in catalyzing recovery, and that certain preconditions or concurrent conditions may be needed to ensure that these 'virtuous cycles' have a positive feedback effect on the other two dimensions of fragility, improving development and security outcomes and catalyzing long-term recovery in state authority, legitimacy, and capacity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".