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Record W4280619788 · doi:10.1080/13604813.2022.2067719

‘Don’t wake papa bear!’ Understanding media representations of landlord-tenant relations

2022· article· en· W4280619788 on OpenAlexfundaboutno aff
Danielle Kerrigan

Bibliographic record

VenueCity · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLandlordHegemonyMasculinityFemininityScholarshipPaternalismSociologyRetrenchmentGovernment (linguistics)Political economyGender studiesPolitical sciencePoliticsPublic administrationLaw

Abstract

fetched live from OpenAlex

Landlord–tenant relations are one of the core social relations of daily life yet are surprisingly under-theorized by housing scholars and geographers. This article begins to address this gap by applying for feminist scholarship on hegemonic masculinity and emphasized femininity to the case of the expansion and subsequent retrenchment of rent-control policy in Ontario, Canada in 2017–2018. Through a discourse analysis of government policy documents and news media coverage, I demonstrate that portrayals of landlords and tenants broadly conformed to characteristics of hegemonic masculinity and emphasized femininity, respectively, with landlords most commonly portrayed as ‘rational’ and tenants most commonly portrayed as ‘vulnerable’. Landlords benefit from traits associated with hegemonic masculinity even if they themselves do not embody them. Similarly, landlords benefit from the portrayal of tenants as passive victims, in need of paternalistic government protection, as opposed to potentially powerful collective actors.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.015
Scholarly communication0.0080.009
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.088
GPT teacher head0.247
Teacher spread0.159 · 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 designQualitative
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

Citations8
Published2022
Admission routes2
Has abstractyes

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