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Record W7127227963

Navigating Climate-Induced Migration and Displacement: Policy Pathways in Canada's Immigration Framework

2025· dissertation· en· W7127227963 on OpenAlexaboutno aff
Caroline Minh Ngoc Nguyen

Bibliographic record

VenueQSpace (Queen's University Library) · 2025
Typedissertation
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationClimate changeGovernment (linguistics)Context (archaeology)RefugeeImmigration policyIntersection (aeronautics)
DOInot available

Abstract

fetched live from OpenAlex

This thesis provides a comprehensive examination of Canada's immigration system, analyzing its historical development and current state in the context of climate-induced migration. The study addresses an urgent and evolving global issue: the intersection of climate change and human mobility. It highlights how Canada, despite its long history of immigration and strong emphasis on refugee protection, faces significant challenges in adequately preparing for and responding to the growing number of people displaced by climate change, both internally and internationally. The study critically assesses existing policies, drawing comparisons with Australia and New Zealand, and identifies the gaps and limitations within Canada's current framework. It emphasizes the need for specific policy adjustments to better accommodate climate migrants, considering the distinctions between sudden and slow-onset environmental changes. The research suggests that while Canada has committed to high immigration targets, its current system is not yet equipped to handle the unique complexities of climate migration, which will likely increase pressure on its humanitarian programs. By analyzing Canada's current policies and proposing concrete adjustments, this study offers a roadmap for policymakers to proactively adapt Canada's immigration system to the realities of climate migration. This study is primarily a review of existing academic and government literature on the subject. It not only contributes to academic discourse on the topic but also provides actionable insights for creating a more just and effective system for those displaced by environmental factors.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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 designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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