MétaCan
Menu
Back to cohort
Record W3105678377 · doi:10.3390/su12229527

Navigating Climate Change: Rethinking the Role of Buildings

2020· article· en· W3105678377 on OpenAlexaff
Raymond J. Cole

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAgency (philosophy)Adaptation (eye)Government (linguistics)Top-down and bottom-up designClimate changeBuilt environmentArchitectural engineeringBuilding designScale (ratio)Environmental planningPolitical sciencePublic relationsEngineeringSociologyCivil engineeringGeographyPsychologyEcologySocial science

Abstract

fetched live from OpenAlex

This paper focuses on the design of buildings as part of society’s response to the climate crisis in the aftermath of the COVID-19 pandemic. It draws on a broad literature to address two interrelated goals—first, to align regenerative development and design with the necessary bottom-up adaptation strategies and human agency, and second, to identify new, broader possible roles of buildings and responsibilities of design professionals. This required a comparison of current green building and emerging regenerative approaches and identifying the relevant characteristics of top-down and bottom-up mechanisms. The paper accepts that adaptation to climate change will, to a large extent, depend on people’s day-to-day actions in the places they live, and argues that the built environment will have to be infused with the capability to enable inhabitants’ greater agency. Viewing buildings as playing a connective role in the existing urban fabric seriously challenges the primacy of the individual building as the focus of environmental strategies. The roles of building design professionals will likely expand to include mediating between top-down imposed government controls and increasing bottom-up neighborhood-scale social activism.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.026
GPT teacher head0.328
Teacher spread0.302 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations23
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicCOVID-19 impact on air qualityFrench-language works237,207