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Record W7014695366

Retrofitting Concrete Utopias: Climate Change Adaptation for Mid-Century Housing Stock

2023· other· en· W7014695366 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeRetrofittingStock (firearms)PopulationFuel povertyPublic housingNegotiationPoliticsPolitical economy of climate change
DOInot available

Abstract

fetched live from OpenAlex

Toronto is the site of nearly two thousand 1960s concrete residential tower blocks in various states of maintenance, in various locations, more or less peripheral to the major public transit corridors, housing in many cases vulnerable populations on the peripheries of the economic core of the city. Overcrowding of apartments, lack of affordability, inadequate maintenance of basic amenities has been identified as significant problems in academic and social agency reports. This paper is concerned with extreme heat events related to climate change and mortality especially for vulnerable population in these legacy towers. My contribution to this discussion takes as its framework of analysis an understanding that social processes are sociopolitical negotiations in uneven relative power relationships. They are political and environmental. This project is driven by concerns for the experiences of human well-being in the face of the global climate emergency, efforts at reducing operational carbon emissions, and energy consumption, for cooling especially. Comparable towers in France and Switzerland, as well as low rises there and in Germany, are examined, buildings that have undergone significant retrofitting to address these issues. The towers and site analyses are approached within their specific locations, the natural environment and the social infrastructure within which they stand. Practical learnings from European cases and current practices in Toronto lead to practical policy recommendations that aim to bolster institutional and financial capacity in the Toronto situation to address the dual crises of affordable housing and climate change mitigation.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.475
Threshold uncertainty score0.944

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 studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0190.001

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 head0.038
GPT teacher head0.197
Teacher spread0.159 · 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 designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

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