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Landlords and Scattered-Site Housing

2017· book-chapter· en· W2588952916 on OpenAlexaboutno aff
Timothy MacLeod, Tim Aubry, Geoffrey Nelson, Henri Dorvil, Scott McCullough, Patricia O’Campo

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsLeasehold estateSupportive housingPublic housingHousing FirstBusinessPublic relationsPolitical scienceEconomic growthMedicineNursingEconomics

Abstract

fetched live from OpenAlex

The literature on landlords in independent supportive housing is surprisingly sparse, given that landlords are fundamental to the coordination and provision of housing for participants in programs like Housing First. Landlords are important stakeholders in housing programs because they directly impact program participants’ ability to get and keep housing. Additionally, landlords represent novel stakeholders in housing and services for program participants insofar as they are “normal” community members contractually related to participants through tenancy agreements, but with no clinical role. This chapter reviews the roles, experiences, and needs of landlords in independent supportive housing. The chapter begins with a review of literature on landlords and independent supportive housing. Next, the main body of the chapter focuses on research with landlords in four sites of the Canadian At Home/Chez Soi Housing First initiative. The chapter concludes with lessons learned from the At Home/Chez Soi experience.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.157
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.007
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.001

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 head0.195
Teacher spread0.158 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2017
Admission routes1
Has abstractyes

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Same venueOxford University Press eBooksSame topicHousing, Finance, and NeoliberalismFrench-language works237,207