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Record W4200063533 · doi:10.17645/up.v6i4.4399

Equity in Urban Climate Change Adaptation Planning: A Review of Research

2021· review· en· W4200063533 on OpenAlexaff
Kayleigh Swanson

Bibliographic record

VenueUrban Planning · 2021
Typereview
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEquity (law)Vulnerability (computing)DisadvantagedClimate changeSocial vulnerabilityAdaptation (eye)Environmental justiceSocial equalityPolitical scienceEnvironmental planningEnvironmental resource managementPublic economicsSociologyGeographySocial psychologyPsychologyEconomics

Abstract

fetched live from OpenAlex

A growing number of cities are preparing for climate change by developing adaptation plans, but little is known about how these plans and their implementation affect the vulnerability of groups experiencing various forms of underlying social inequity. This review synthesizes research exploring the justice and equity issues inherent in climate change adaptation planning to lay the foundation for critical assessment of climate action plans from an equity perspective. The findings presented illuminate the ways in which inequity in adaptation planning favours certain privileged groups while simultaneously denying representation and resources to marginalized communities. The review reveals the specific ways inequity is experienced by disadvantaged groups in the context of climate change and begins to unpack the relationship between social inequity, vulnerability, and adaptation planning. This information provides the necessary background for future research that examines whether, and to what extent, urban adaptation plans prioritize social vulnerability relative to economic and environmental imperatives.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.010
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.697
GPT teacher head0.546
Teacher spread0.151 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations68
Published2021
Admission routes1
Has abstractyes

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