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Record W4293155591 · doi:10.1080/13549839.2022.2100878

Mobilizing infrastructure investments for urban climate action in Africa: enabling factors for multilevel action

2022· article· en· W4293155591 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.

Bibliographic record

VenueLocal Environment · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsConstructiveLegislatureSustainabilityClimate changeTransformative learningAction planAction (physics)Sustainable developmentBusinessEnvironmental resource managementEnvironmental planningScale (ratio)Economic growthPolitical scienceEconomicsSociologyGeography

Abstract

fetched live from OpenAlex

This article explores the importance of national governments and national-local relationships for scaling up local climate action to achieve global goals. From the Sustainable Development Goals to the New Urban Agenda to the Paris Climate Change Agreement, the achievement of global sustainability goals will depend on deep changes to national infrastructure and urban systems. Through an analysis of climate action planning and investments in exemplary cases in Africa, the paper highlights the opportunities and challenges that come with integrating national governments into urban-focused priorities and needs, especially for mobilising financial resources. The paper finds that scaling up city climate action in the selected African countries benefits from a constructive multi-level relationship between local and national institutions and stakeholders to shape and improve the legislative, financial and operating frameworks to enable systemic change. Large-scale urban climate action can be enabled by formal multi-level institutional arrangements, links to politically prioritised policy frameworks, and transformative aims that address both climate and socio-economic benefits for communities.

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)0.000
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 score0.806
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.094
GPT teacher head0.298
Teacher spread0.204 · 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