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Record W2992997300 · doi:10.36939/cjur/vol27no2/art131

The Ever-Shrinking Condo

2018· article· en· W2992997300 on OpenAlexaffvenueabout
Marc Vachon

Bibliographic record

VenueCanadian journal of urban research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsHumanitiesSquare (algebra)GeographyArt

Abstract

fetched live from OpenAlex

Between 2005 and 2010, the average size of a new condo oscillated between 875 and 925 square feet (Perkins2014). By 2015, a new condo average size is 797 square feet according to RealNet Canada. During this period, we witnessed in Vancouver, Toronto and Montréal, the rise of the micro-condo, which varies, from 226 square feet to 395 square feet. This article examines potential economic, demographic and cultural causes and consequences of the rise of micro-condos and their impact on the urban landscape and public space. RésuméEntre 2005 et 2010, la taille moyenne d’un nouveau condo a oscillé entre 875 et 925 pieds carrés (Perkins,2014). Par l’an 2015, la taille moyenne d’un nouveau condo est de 797 pieds carrés selon RealNet Canada. Aucours de cette période, nous assistons à Vancouver, Toronto et Montréal à l’essor du micro-condo, qui varie de 226 pieds carrés à 395 pieds carrés. Cet article examine les causes potentielles et conséquences économiques, démographiques et culturelles de la montée en des micro-condos et son impact sur le paysage urbain et l’espace public.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.338
Threshold uncertainty score0.679

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.074
GPT teacher head0.343
Teacher spread0.269 · 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 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

Citations0
Published2018
Admission routes3
Has abstractyes

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