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Record W2936480753 · doi:10.51357/cs.v13i2.126

Intersectionality Analysis, the Welfare State and Women's Health

2018· article· en· W2936480753 on OpenAlexafffundabout
Toba Bryant, Dennis Raphael

Bibliographic record

VenueCritical Studies An International and Interdisciplinary Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsOntario Tech UniversityYork University
FundersUniversity of Ontario Institute of Technology
KeywordsIntersectionalityWelfare stateSocial securitySocial policySocial determinants of healthSocial inequalityPolitical scienceHealth equityWelfarePopulationSociologyInequalityPoliticsEconomicsEconomic growthHealth careGender studiesLaw

Abstract

fetched live from OpenAlex

This article applies intersectionality analysis to consider women's health and well-being in Canada's welfare state with attention to those occupying vulnerable social locations. Political and economic structures and processes associated with different forms of the welfare state are responsible for producing these vulnerabilities as they differentially distribute economic and social resources amongst the population. Inequities in these distributions create the social inequalities that act through the social determinants of health to spawn health inequalities. The liberal welfare state -- with its dominant institution being the marketplace -- has higher levels of these inequalities than social democratic and conservative welfare states with rather less public policy effort to reduce them. In addition, the acceptance of neoliberalism as a governing ideology has seen Canadian and other governments further reducing the State role in managing the economy and providing economic and social security to citizens. This has had particular implications for those occupying vulnerable social locations such that the intersectionality concept -- combined with welfare state analysis -- provides a lens which can both explan these social and health inequalities and suggest means to reduce them.

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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.003
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.062
GPT teacher head0.472
Teacher spread0.409 · 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 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

Citations2
Published2018
Admission routes3
Has abstractyes

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