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Record W4230568094 · doi:10.32920/ryerson.14656965

Expanding within flexible housing for growing families in urban neighbouthood

2021· preprint· en· W4230568094 on OpenAlexaff
Tae‐Hoon Kim

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsUrban sprawlPopulationEconomic growthEnvironmental planningDevelopment economicsGeographyUrban planningSociologyEconomicsEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Two- thirds of the world’s population will be living in urbanized areas by 2050. The response to this trend in housing demand has been intensification of the urban core, or sprawl. However, this solution addresses only current conditions and does not allow for future change. People’s housing needs are greatly influenced by their stages of life and by socio-economic factors that are constantly changing over time. However, most housing offers unchanging physical environments. Therefore, there is a conflict the between dynamic nature of people’s lifestyle and their dwellings. Living in a fast-paced society where change is inevitable, how can we design future housing that responds to the evolving needs and desires of diverse households throughout their life cycle? This thesis argues that homes should not be designed with a single purpose. Instead, they must be flexible and open-ended, and lend themselves conveniently to transform.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.050
GPT teacher head0.255
Teacher spread0.206 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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