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Vulnerability Assessment of Developing Countries: The Case of Small‐island Developing States

2007· article· en· W1998971033 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

VenueDevelopment Policy Review · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsNipissing University
Fundersnot available
KeywordsVulnerability (computing)Vulnerability indexSmall Island Developing StatesFraming (construction)Vulnerability assessmentGeographyUrbanizationDeveloping countryIndex (typography)Composite indexEnvironmental resource managementPerspective (graphical)Economic geographyRegional scienceEnvironmental planningEconomic growthComposite indicatorClimate changeEnvironmental scienceComputer sciencePsychological resilienceEconomicsComputer securityEcologyEconometrics

Abstract

fetched live from OpenAlex

This article puts forward a spatial perspective in framing the methodology for vulnerability assessment (VA) of developing countries, with special reference to small‐island developing states (SIDS). Geographic vulnerability from a developing‐world perspective is defined by the country's susceptibility to physical and human pressures, risks and hazards in temporal and spatial contexts. In constructing the composite vulnerability index (CVI), four core indicators are selected as sub‐indices. The study confirms the vulnerability of SIDS based on four dimensions, namely, coastal index (G1), peripherality index (G2), urbanisation indicator (G3) and vulnerability to natural disasters (G4), and advocates consideration of place vulnerability and temporal distinctions when assessing the vulnerability of SIDS in particular.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.181
GPT teacher head0.438
Teacher spread0.257 · 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