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Record W1991039288 · doi:10.1080/07388940802397509

State Fragility and Implications for Aid Allocation: An Empirical Analysis

2008· article· en· W1991039288 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConflict Management and Peace Science · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsCarleton University
Fundersnot available
KeywordsFragilityLegitimacyCLARITYExtant taxonEmpirical researchPovertyState (computer science)TerrorismPublic economicsEconomicsPositive economicsPolitical sciencePoliticsComputer scienceEconomic growthLaw

Abstract

fetched live from OpenAlex

In recent years, state fragility has gained importance as a result of the perceived links between poverty, conflict, and global terrorism. In this paper, we examine the relationship between state fragility and aid by evaluating the literature and research programs currently extant. We bring conceptual clarity to the issue by developing and testing an alternative theoretical framework using CIFP's fragility index (articulated around the concepts of authority, legitimacy, and capacity [ALC]) and by using data collected for the period 1999—2005 to identify the empirical determinants of fragility. We then examine the effects of state fragility on aid allocation, using the ALC framework as defined. Our results indicate that aid allocation is directed toward states on the basis of their capacity and authority scores and not on the basis of their legitimacy scores. Finally, we assess the theoretical and policy implications of these findings and specify directions for future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.384
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it