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Record W4229001471 · doi:10.51952/9781447352167.ch005

Comparative analysis

2022· book-chapter· en· W4229001471 on OpenAlexaboutno aff
Jean‐Louis Denis, Sabrina Germain, Catherine Régis, Gianluca Veronesi

Bibliographic record

VenuePolicy Press eBooks · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicQuality of Life Measurement
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

In this section, we present a set of analytical themes and considerations derived from our analysis of the three empirical cases in England and Canada. The intent is to elucidate the implications of our research on how we understand the medical doctor–healthcare reform nexus and to test our theoretical model’s ability to explain key variations and points of convergence across the cases. We first examine the impact on healthcare reforms of the deals and policy parameters set at the inception of PFHS. We identify foundational elements that set the scene for future debates and negotiations between the government and medical doctors in the development of reforms. Contextual factors push governments into this most significant health reform, and the creation of PFHS is a revelatory moment. It shows how the two protagonists become engaged in a common endeavour with different expectations and abilities to influence the architecture of the system. The spirit of the initial agreement and the growing interdependence between governments and the medical profession has enduring implications for their future relationship. Second, we delineate how governments address core policy dilemmas in the context of PFHS. Manifestations of the agency of governments within the mediated space of reforms are shaped by intense political pressures to respond to dilemmas such as escalating costs and problems with access to care. They also interface with the medical profession’s reactions to reformative propositions. On the one hand, governments need to secure the collaboration of a powerful insider, the medical profession, and mobilise a diversity of policy instruments that go beyond coercion.

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)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.896
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0100.020
Science and technology studies0.0070.002
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1040.006

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 head0.313
GPT teacher head0.435
Teacher spread0.122 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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