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Record W2316652889 · doi:10.1136/bmj.e3835

Canadian doctors try to garner public support to fight pay freeze

2012· article· en· W2316652889 on OpenAlexaffabout
B. Kermode-Scott

Bibliographic record

VenueBMJ · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Challenges
Canadian institutionsCalgary Laboratory Services
Fundersnot available
KeywordsData scienceComputer sciencePublic relationsMedicinePolitical science

Abstract

fetched live from OpenAlex

A dispute between the government of Ontario and the province’s medical profession over the remuneration of doctors that exploded in early May has spread to the rest of Canada. The Ontario government and Ontario Medical Association have sought and gained allies from across Canada as they engage in a battle to win public opinion. Ontario’s provincial government took the first shot in the current war of words on 7 May 2012 when Premier Dalton McGuinty’s government withdrew from negotiations with the medical profession and announced that Ontario was imposing “a real wage freeze” for doctors to invest “more …

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)0.009
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.825

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0300.011
Scholarly communication0.0120.004
Open science0.0030.007
Research integrity0.0140.016
Insufficient payload (model declined to judge)0.0170.003

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 head0.167
GPT teacher head0.474
Teacher spread0.307 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2012
Admission routes2
Has abstractyes

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