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Record W2524683555 · doi:10.2495/safe-v6-n3-582-588

Vulnerabilities of coastal communities resulting from climate change: a case study of San Mateo, Belize

2016· article· en· W2524683555 on OpenAlexvenueno aff
Andrew Reid Bell, R. Duggleby, Anderson Kinch

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeEnvironmental planningGeographyEnvironmental resource managementEnvironmental scienceOceanographyGeology

Abstract

fetched live from OpenAlex

This paper focuses on the migration patterns of residents of the community of San Mateo, San Pedro, Belize.Recent research conducted in the community of San Mateo by Florida State University faculty and students revealed numerous vacant lots along the coast and in areas where water has risen and not been displaced.In total, the vacant lots accounted for nearly a quarter of the community.Drawing from these findings, this paper argues that these vacant lots are due to environmental changes in the community, most likely attributed to climate change.This paper will examine environmental migration in San Mateo as well as availability of resources such as electricity and water.The ultimate goal of this research is to assess how the resettlement of environmental migrants is impacted by the availability of resources.The paper will subsequently illustrate that Central America is especially prone to the effects of climate change, and that this directly impacts the most vulnerable, coastal communities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.622

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.314
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2016
Admission routes1
Has abstractyes

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Same venueInternational Journal of Safety and Security EngineeringSame topicClimate Change, Adaptation, MigrationFrench-language works237,207