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Record W2234517310 · doi:10.1097/mlr.0000000000000500

Blunt Policy Instruments Deliver Blunt Policy Outcomes

2016· letter· en· W2234517310 on OpenAlexaffabout
Beverley M. Essue, Stephen Birch

Bibliographic record

VenueMedical Care · 2016
Typeletter
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBluntResearch centrePolitical scienceHealth policyHealth economicsLibrary scienceHealth centrePublic administrationMedicineHealth careFamily medicineLawSurgeryComputer science

Abstract

fetched live from OpenAlex

*Centre for Health Economics and Policy Analysis, McMaster University †Menzies Centre for Health Policy, University of Sydney ‡Centre for Health Economics, University of Manchester B.M.E. is the recipient of a Sidney Sax Research Fellowship from the National Health and Medication Research Council of Australia. She has no conflicts of interest to declare. S.B. has no conflicts of interest to declare. Reprints: Beverley M. Essue, PhD, MPH, McMaster University, CHEPA, CRL Building 282, 1280 Main Street, Hamilton, ON, Canada L8S 4K1. E-mail: [email protected].

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)0.020
metaresearch head score (Gemma)0.103
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.048
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.103
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0480.006

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 head0.443
GPT teacher head0.571
Teacher spread0.128 · 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
GenreCommentary

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

Citations4
Published2016
Admission routes2
Has abstractyes

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