MétaCan
Menu
Back to cohort
Record W3088791636 · doi:10.1177/2399654420960489

Contracting-out care: The socio-spatial politics of nursing home care at the intersection of British Columbia’s labor, land, and capital markets

2020· article· en· W3088791636 on OpenAlexafffundabout
CS Ponder, Andrew Longhurst, Margaret J. McGregor

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsUniversity of British ColumbiaCanadian Centre for Policy Alternatives
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLegislatureLegislationPoliticsPrivate sectorHealth careBusinessInvestment (military)DebtCapital (architecture)Order (exchange)Public administrationFinanceEconomic growthLabour economicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

The provincial health services labor market was fundamentally altered in 2002 with the introduction of a series of legislative and policy changes enabling the contracting-out, or subcontracting, of care workers in nursing home facilities in order to encourage private sector investment in nursing home infrastructure and provision. This legislation was intended to shrink provincial expenses and replace aging facilities through partnerships with the private sector that would keep debt off provincial books. Through in-depth interviews with front-line workers and provincial and Health Authority administrators, this research foregrounds care as a political relationship by mapping how these legislative changes related to provincial budget concerns splintered a specialized labor market, eroding both working and caring conditions, and exposing eldercare in British Columbia, Canada to the speculative dynamics of finance.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.882

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0240.019
Scholarly communication0.0100.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.282
Teacher spread0.260 · 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

Citations10
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueEnvironment and Planning C Politics and SpaceSame topicHealthcare innovation and challengesFrench-language works237,207