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Record W6996298519

Revolution Within A Revolution: Québec's experiment with co-operative health care & social service delivery

2007· article· en· W6996298519 on OpenAlexaboutno aff

Bibliographic record

VenueAUSpace (Athabasca University) · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)Health careSolidarityAction (physics)Intervention (counseling)Service (business)Health servicesDiscountingAgency (philosophy)Health insurance
DOInot available

Abstract

fetched live from OpenAlex

Québec's decade of experimentation with health care and social service co-operatives has given rise to a reconfiguration of the actors in the health system. No longer do people talk about a system with two actors. Rather than wait for the State or for physician-entrepreneurs to supply needed services, more and more citizens are taking effective action through the structure of the solidarity co-op or that of the nonprofit community-based organization. Without discounting the importance of the state in health care, Girard invokes economist Gilles Paquet who “Forget the Quiet Revolution” whose analysis of state intervention in the 60’s across numerous sectors of Quebec society leads to his conclusion that a new social consciousness is required – “one that prizes initiative and local development and eases the grip of State supervision and protection”. Girard demonstrates that concrete results are being achieved. However, he points out that success it is not a foregone conclusion, citing several examples on the other side of the ledger. Nevertheless, the problems in the health care system and a steadily aging demographic will, Girard believes, lead to a multiplication of initiatives that reconfigure the relationship between citizens, professionals, insurance claimants and the community.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.988

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 studies0.0010.000
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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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 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
Published2007
Admission routes1
Has abstractyes

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