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Record W2889364609 · doi:10.1177/0020731418795136

Governmental Illegitimacy and Incompetency in Canada and Other Liberal Nations: Implications for Health

2018· article· en· W2889364609 on OpenAlexaffabout
Dennis Raphael, Morris Komakech, Toba Bryant, Ryan Torrence

Bibliographic record

VenueInternational Journal of Health Services · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsOntario Tech UniversityYork University
Fundersnot available
KeywordsWelfare stateInequalityState (computer science)WelfarePolitical scienceSocial WelfareDeveloping countryPolitical economyDevelopment economicsEconomicsSociologyEconomic growthPoliticsLaw

Abstract

fetched live from OpenAlex

The welfare state literature on developing nations is concerned with how governmental illegitimacy and incompetency are the sources of inequality, exploitation, exclusion, and domination of significant proportions of their citizenry. These dimensions clearly contribute to the problematic health outcomes in these nations. In contrast, developed nations are assumed to grapple with less contentious issues of stratification, decommodification, and the relative role of the state, market, and family in providing economic and social security, also important pathways to health. There is an explicit assumption that governing authorities in developed nations are legitimate and competent such that their citizens are not systematically subjected to inequality, exploitation, exclusion, and domination by elites. In this article, we argue that these concepts should also be the focus of welfare state analysis in developed liberal welfare states such as Canada. Such an analysis would expose how public policy is increasingly being made in the service of powerful economic elites rather than the majority, thereby threatening health. It would also serve to identify means of responding to these developments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.395

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 studies0.0000.000
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 head0.030
GPT teacher head0.422
Teacher spread0.392 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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