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Record W4206973897 · doi:10.54825/jxnk4028

When “Just Right” Is Not Right: Pushing back against austerity in Toronto

2019· article· en· W4206973897 on OpenAlexaffabout
Mehedi Khan, Douglas L. Young

Bibliographic record

VenueRadical Housing Journal · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsYork University
Fundersnot available
KeywordsAusterityNeoliberalism (international relations)Public housingPublic administrationPovertyGovernment (linguistics)Corporate governancePolitical scienceGeneral partnershipRight to the cityPolitical economySociologyEconomic growthPoliticsEconomicsLawManagement

Abstract

fetched live from OpenAlex

In Toronto, selected social housing communities are being ‘revitalized’ in partnership with the private sector. This move is widely embraced by policymakers, politicians and social service agencies all of whom applaud its utility in transforming neighbourhoods of concentrated poverty into mixed-income communities. However, residents and community groups criticize this form of top-down neoliberal governance as regulating the morality of space. We explore here the met and unmet demands of residents of three social housing communities targeted for revitalization in Toronto, and highlight resident wins and losses and share lessons for housing activists involved in other similar situations. We see this provocation as part of a multi-scalar push back against austerity in housing. Specifically, while the 2008 crisis did not directly affect Canadian housing, it did play a role in solidifying austerity-as-common-sense as the over-arching frame of neoliberal governance at the local, provincial and national scales. As the examples in this article underscore, this has had significant and damaging impacts on hegemonic housing policy, with government action on housing being strategically limited in ambition.

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)0.001
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.030
GPT teacher head0.242
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

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 routes2
Has abstractyes

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