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Record W3122858862 · doi:10.1177/0308518x20983152

Beyond crisis? Using rent theory to understand the restructuring of publicly funded seniors’ care in British Columbia, Canada

2021· article· en· W3122858862 on OpenAlexafffundabout
Kendra Strauss

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFinancializationEconomic rentRestructuringEconomicsRent-seekingArgument (complex analysis)MainstreamPoliticsPolitical economyMarket economyPolitical scienceFinance

Abstract

fetched live from OpenAlex

Crises of seniors' care in countries like the UK and Canada, further highlighted by the COVID-19 pandemic, have been connected to processes of privatization and financialization. In this paper I argue that rent theory is important for disaggregating mechanisms, including of accumulation by dispossession, the devaluation of labour, and assetization, that underpin the process of financialization in the sector. Work on rents often divides between critical approaches, especially to land rent, and mainstream institutionalist and public choice approaches to rent-seeking. Critical rent theory is evolving beyond this divide to understand a broader range of types of rent. Yet, despite attention to the increasing importance of economic rents and forms of rentierism, labour and social reproduction are often excluded from the analysis of how rent relations arise. This paper demonstrates the problems with these exclusions. The argument is illustrated through an analysis of the restructuring of eldercare in British Columbia, Canada, in the last two decades, and employs a feminist political economy approach to examine the social production of rent relations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.182
Teacher spread0.167 · 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

Citations35
Published2021
Admission routes3
Has abstractyes

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