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Record W2189417168

Cities, Climate Change and the Green Economy: A Thematic Literature Survey

2022· report· en· W2189417168 on OpenAlexaff
Stephen McBride, John Shields, Stephanie Tombari

Bibliographic record

VenueYork University Digital Library (York University) · 2022
Typereport
Languageen
FieldEnvironmental Science
TopicSustainable Development and Policies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsClimate changePolitical sciencePublic administrationPoliticsUrbanizationGreen economyPolitical economy of climate changeEconomic growthPublic policySustainable developmentEconomics
DOInot available

Abstract

fetched live from OpenAlex

This working paper constitutes an extensive review of the literature concerned with exploring the role of cities in addressing climate change and green employment creation. It identifies five key areas for discussion: (1) greening the local economy; (2) shifting local policy roles and trends in urbanization; (3) policy learning and cross-jurisdictional collaboration; (4) the place of civic participation and engagement; and, (5) the co-benefits of a green economy. These areas will be addressed in an effort to critically explore the following questions: What impacts do cities have on climate change? What role are cities currently playing with regards to the development and implementation of climate change and green economic policies? What barriers do cities face with regards to developing and implementing climate change and green economic policies? What potential is there for policy development?

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)0.002
metaresearch head score (Gemma)0.004
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 score0.014
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0140.036
Science and technology studies0.0020.002
Scholarly communication0.0050.007
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.167
Teacher spread0.144 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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