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Record W4392584536 · doi:10.1016/j.oneear.2024.02.002

Research priorities for climate mobility

2024· article· en· W4392584536 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOne Earth · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsInternational Development Research CentreCarleton University
FundersNational Research FoundationInternational Development Research Centre
KeywordsEnvironmental scienceGeographyComputer science

Abstract

fetched live from OpenAlex

The escalating impacts of climate change on the movement and immobility of people, coupled with false but influential narratives of mobility, highlight an urgent need for nuanced and synthetic research around climate mobility. Synthesis of evidence and gaps across the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report highlight a need to clarify the understanding of what conditions make human mobility an effective adaptation option and its nuanced outcomes, including simultaneous losses, damages, and benefits. Priorities include integration of adaptation and development planning; involuntary immobility and vulnerability; gender; data for cities; risk from responses and maladaptation; public understanding of climate risk; transboundary, compound, and cascading risks; nature-based approaches; and planned retreat, relocation, and heritage. Cutting across these priorities, research modalities need to better position climate mobility as type of mobility, as process, and as praxis. Policies and practices need to reflect the diverse needs, priorities, and experiences of climate mobility, emphasizing capability, choice, and freedom of movement.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.357
GPT teacher head0.451
Teacher spread0.094 · 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