MétaCan
Menu
Back to cohort
Record W4400672874 · doi:10.25071/2817-5344/70

‘Canadianism,’ the Welfare State, and policy growth

2024· article· en· W4400672874 on OpenAlexaffabout
Diana Gabrielle Koujanian

Bibliographic record

VenueCanadian Journal for the Academic Mind · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsCarleton University
Fundersnot available
KeywordsWelfare stateWelfareState (computer science)EconomicsBusinessPolitical scienceMarket economyComputer scienceLawPolitics

Abstract

fetched live from OpenAlex

This paper will explore Canadianism and its relationship to Universal healthcare. Canadianism, a term derived for the purpose of this text, is used to conceptualize an ‘Idea’ born out of Canadian identity and Economic Nationalism during commonwealth movements of the later 20th century. The Idea is studied to understand how Canadian civil society favors Universal systems, particularly in this paper healthcare, over private initiative. This paper will assess the roots of the privatization debate and argue the rivalrous nature between Canadianism and New Public Management [NPM]. A key deliberation will be had on the significant role that ethics plays in Canadianism, and how this had success in limiting the influence of NPM on Healthcare. This paper will also examine a current ‘privatization’ case, Bill C-60, and its potential threat to Keynesian Economics’ opportunity-for-all approach to healthcare. A second key deliberation will be had on the concept of ‘Trust,’ how it informs Canadianism and why this makes Bill C-60’s discourse convoluted. Conclusively, a discussion will be had on the issues with Canadianism in healthcare discourse through considering semantics and policy growth. The limits of Canadianism will be briefly highlighted. This paper finds that Canadian is essential to comprehend when considering why healthcare reform in Canada is difficult to manage.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.319
Teacher spread0.293 · 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.

Study designNot applicable
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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal for the Academic MindSame topicCanadian Policy and GovernanceFrench-language works237,207