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Record W2594467543 · doi:10.22329/wyaj.v33i2.4841

BUILDING INTERNATIONAL APPROACHES TO CLIMATE CHANGE, DISASTERS, AND DISPLACEMENT

2017· article· en· W2594467543 on OpenAlexfundvenueno aff
Jane McAdam

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
FundersNorges ForskningsrådUniversity of Windsor
KeywordsScope (computer science)Vulnerability (computing)Climate changeVariety (cybernetics)Context (archaeology)Disaster risk reductionHazardPsychological interventionForced migrationPolitical scienceEnvironmental planningEnvironmental resource managementGeographyComputer scienceComputer securityRefugeeLawEconomicsPsychology

Abstract

fetched live from OpenAlex

On average, one person is displaced each second by a disaster-related hazard. Most people move within their own countries, but some are forced across international borders. This article outlines the scope of existing international legal frameworks to assist people displaced in the context of disasters and climate change, and suggests a variety of different tools that are required to address the phenomenon. Legal, policy, technical and scientific interventions, including disaster risk reduction, climate change adaptation and mitigation, development, and migration opportunities, will determine whether, and for how long, people can remain in their homes, and whether doing so enables them to lead dignified lives or exposes them to risks and increased vulnerability. Identifying the need for a broad, complementary set of policy strategies necessarily affects how international law should be progressively developed in this area.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.537

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.376
GPT teacher head0.402
Teacher spread0.026 · 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.

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

Citations7
Published2017
Admission routes2
Has abstractyes

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