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Record W4392192563 · doi:10.4324/9781003333975

Sustainable Housing in a Circular Economy

2024· book· en· W4392192563 on OpenAlexaboutno aff
Naomi Keena, Avi Friedman

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsCircular economyBusinessEconomicsEcologyBiology

Abstract

fetched live from OpenAlex

This book relates circular economy principles to housing design and construction and highlights how those principles can result in both monetary savings, positive environmental impact, and socio-ecological change. Chapters focus on three key circular economy principles and apply them to architectural construction and design, namely rethinking of the end-of-use phase of a building and the potential of design-for-disassembly; the role of digitization and data standardization in fostering evidence-based circular economy design decision-making; and presenting space as a resource to conserve, via exploration of the sharing economy and flexibility principles. Beyond waste management and material cycles, this book provides a holistic understanding of the opportunities across the building life cycle that can allow for sustainable and affordable circular housing. With case studies from 13 different countries, including but not limited to the Hammarby Sjöstad district in Sweden, the Circle House in Denmark, Benny Farm in Canada, VMD Prefabricated House in Mexico, and the Deep Performance Dwelling in China, authors pair theoretical frameworks with real-world examples. This will be a useful resource for upper-level students and academics of architecture, construction, and planning, especially those studying and researching housing design, building technology, green project management, and environmental design.

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)0.000
metaresearch head score (Gemma)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.004

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 head0.025
GPT teacher head0.264
Teacher spread0.239 · 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
GenreOther

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

Citations11
Published2024
Admission routes1
Has abstractyes

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