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Record W4385256238 · doi:10.29173/cjnser586

Can They Build or Not? Nonprofit Housing Development in an Era of Government Re-engagement

2023· article· en· W4385256238 on OpenAlexafffundvenueabout
Aijia Deng, Catherine Leviten‐Reid, Luc Thériault

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of New BrunswickCape Breton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRentingGovernment (linguistics)Affordable housingBusinessRental housingFlexibility (engineering)Context (archaeology)Public relationsInvestment (military)Housing industryNonprofit sectorEconomic growthMarketingPublic economicsEconomicsPolitical scienceManagementEngineeringPolitics

Abstract

fetched live from OpenAlex

In the context of new government investment in housing, this article explores the experiences of nonprofit organizations in securing support for new affordable rental housing development in three regions across Canada. Many challenges were reported, including ones pertaining to administration (extensive proposal requirements, lack of information and communication, and lengthy review processes), and the design of funding programs (such as a lack of flexibility available to proponents). Participants also reported limitations to the amount and nature of support provided, challenges working across different levels of government, and an uneven playing field among nonprofit and for-profit housing developers. Overall, results show that despite significant and recent investments made available for affordable housing, the nonprofit sector faces many barriers in accessing these, and that significant changes are required so that housing organizations may provide rental units to those in greatest need.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.144
GPT teacher head0.317
Teacher spread0.173 · 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 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

Citations6
Published2023
Admission routes4
Has abstractyes

Explore more

Same venueCanadian journal of nonprofit and social economy researchSame topicHousing, Finance, and NeoliberalismFrench-language works237,207