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Record W3193733451 · doi:10.1080/02673037.2021.1961693

Re-conceptualizing housing tenure beyond the owning-renting dichotomy: insights from housing and financialization

2021· article· en· W3193733451 on OpenAlexaff
Beibei Zhang

Bibliographic record

VenueHousing Studies · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRentingFinancializationHousing tenureCognitive reframingDialecticSociologyRental housingIdeologyRelation (database)EconomicsBusinessLabour economicsPolitical scienceFinanceLawPoliticsSocial psychologyPsychology

Abstract

fetched live from OpenAlex

Housing tenure has often been taken-for-granted as consisting of dichotomous tenure types of owning and renting. This article critiques the owning-renting dichotomy through the lens of housing finance. It critically engages with three housing research programs wherein the owning-renting dichotomy is deep-seated: (a) the bundle of rights thesis, (b) comparative housing and welfare research, and (c) the ideology of housing. For each of them, the article first provides a brief recapitulation of the literature. It then explicates how they are constrained by the owning-renting dichotomy and why abandoning this dichotomy is necessary to transcend their limitations. Based on this, the article reveals a dialectical relation between owning and renting - the binary-oppositional attributes of owning and renting can be resolved with the changing relation of the occupant to financial markets. The article further proposes to re-conceptualize housing tenure as a relation of the occupant to financial markets and discusses the implications for reframing housing studies.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.026
Scholarly communication0.0040.011
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.254
Teacher spread0.206 · 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 designTheoretical or conceptual
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

Citations13
Published2021
Admission routes1
Has abstractyes

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