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Record W2038982642 · doi:10.1177/0096144206297148

Where will the People Go

2007· article· en· W2038982642 on OpenAlexaffabout
Kevin Brushett

Bibliographic record

VenueJournal of Urban History · 2007
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsRegentPublic housingSlumAllotmentEconomic shortageEconomic growthWelfarePublic administrationAffordable housingPolitical scienceGovernment (linguistics)SociologyEconomicsLawPopulation

Abstract

fetched live from OpenAlex

In the late 1940s and early 1950s, Canadian cities dealt with a growing housing shortage while the federal and provincial governments argued over who would implement the provisions of the 1944 National Housing Act. This was particularly true in Toronto. As Torontonians celebrated the construction of Regent Park, Canada's “Premier Slum Clearance and Public Housing Project,” nearly 1,350 Toronto families were housed in dilapidated old army barracks and staff houses. Until Regent Park, the shelters were Toronto's only rent-geared-to-income housing project. This article challenges the assumptions that Toronto's homeless were “shiftless welfare bums” and examines the strategies shelter residents used to survive the often brutal conditions in which they lived and how they hoped to escape them. Finally, it argues that the inability of municipalities to replace emergency shelters with decent affordable housing reveals the long-standing reluctance of Canadian governments to develop social-housing programs to eliminate homelessness.

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)0.003
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.332
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0170.009
Scholarly communication0.0120.012
Open science0.0020.007
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0700.030

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 head0.036
GPT teacher head0.350
Teacher spread0.314 · 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

Citations14
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Urban HistorySame topicHomelessness and Social IssuesFrench-language works237,207