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Record W4247565608 · doi:10.32920/ryerson.14661021

Informal housing in global cities: case studies on Hong Kong, New York City and Toronto

2021· preprint· en· W4247565608 on OpenAlexaboutno aff
Rui Huang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal cityUrbanizationMegacityInformal settlementsHuman settlementEconomic growthStock (firearms)PhenomenonGeographyPopulationPoliticsGlobalizationPrinciple of legalityPolitical scienceEconomic geographyDevelopment economicsSociologyEconomyEconomics

Abstract

fetched live from OpenAlex

This paper examines the major characteristics and drivers of informal housing in three global cities. Despite each city’s unique path to urbanization, Hong Kong, New York City, and Toronto are experiencing housing issues that are reflective of many developed, wealthy cities around the world. Continued population increase from globalization, rising property costs, and insufficient housing stock has contributed to the persistence of various forms of housing that exist outside of formal processes. The case studies exposed distinctive socio-economic and political drivers that underlies this international phenomenon. The findings revealed a continuum of informal dwelling typologies that span across a spectrum of legality and illegality, ranging from highly visible structures to those that are more hidden. This research responds to the need to understand shared challenges between cities in the East and in the West, and may be particularly relevant to city builders who are concerned about distributive justice. Key words: informal housing, informal settlements, global cities, public housing, urban development

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.104
GPT teacher head0.347
Teacher spread0.243 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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