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Record W2134038658 · doi:10.1142/s1464333203001486

DEVELOPING HEALTH IMPACT ASSESSMENT FOR SUSTAINABLE FUTURES IN SMALL ISLAND STATES AND TERRITORIES

2003· article· en· W2134038658 on OpenAlexaboutno aff
Calbert H. Douglas

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsHealth impact assessmentVulnerability (computing)Environmental planningFutures contractEnvironmental impact assessmentFragilityEconomic impact analysisImpact assessmentTourismHealth assessmentBusinessVulnerability assessmentRisk assessmentSmall Island Developing StatesEnvironmental resource managementPolitical scienceGeographyPublic healthEconomicsClimate changeMedicinePsychological interventionComputer science

Abstract

fetched live from OpenAlex

This paper argues that small island states and territories provide a case for the application of health impact assessments. Their characteristic ecological fragility, vulnerability, relatively small size and limited resources give cause for environmental impact concerns. The tendency, therefore, is for decision-makers and developers to focus upon the economic benefits of proposed development projects while mitigating their environmental impacts, paying little attention to health impacts. The paper defines health status and health determinants and provides a toolkit of guidelines for carrying out health impact assessment in small islands. It discusses the approaches and lessons from the UK and Canada by which assessors in small islands can develop health impact assessment processes within their own contexts. The paper identifies the positive and negative health impacts that assessors should consider in assessing the impacts from tourism. The conclusions point to policy implications and the need for decision-makers to incorporate health impact assessment into their respective island's planning and regulatory frameworks.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.329
Teacher spread0.318 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2003
Admission routes1
Has abstractyes

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