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Record W2999091861

Affordable Housing: Exploring Alternative Housing Methods

2019· article· en· W2999091861 on OpenAlexaboutno aff
Reva White

Bibliographic record

VenueYorkSpace (York University) · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsAffordable housingBusinessEconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

For the past three decades, government investments in social and affordable housing in Canada have drastically declined. The Toronto housing market is increasingly expensive and the lower and middle classes struggle to afford quality housing that meets their diverse needs. The housing crisis is explained as an outcome of global and national neoliberalization trends; including the commodification of housing, the dismantling of social supports, the underfunding and discontinuity of social housing programs, and the decline of affordable rentals in Toronto’s housing stock. These structural issues require deep societal transformations and political commitments that are not likely to materialize in the near future. Hence, I argue that alternative housing methods and the strengths of all sectors should be leveraged in the meantime to incentivize and conserve quality affordable housing units. The goal of this Major Project is to, first, understand the current context in Toronto and affordable housing policies, programs, and tools, both current and historical. Second, the paper examines both domestic and international alternative affordable housing typologies, tenures, construction methods, policy models, and financing mechanisms which are underutilized or not used at all in Toronto. The third section dives deeper into opportunities for some of these unique housing methods to be implemented within Toronto, with the lens of legislative applicability and opportunities for implementation. The Major Paper explains why some of these models are underutilized or non-existent within Toronto’s housing ecosystem. It concludes that there is room for innovation in affordable housing, and that cities like Toronto should leverage all housing sectors to provide affordable housing that meets the diverse needs of all residents.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.077
GPT teacher head0.238
Teacher spread0.161 · 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.

Study designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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