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Record W2019258788 · doi:10.1136/bmj.b2900

The double jeopardy of clustered measurement and cluster randomisation

2009· article· en· W2019258788 on OpenAlexaff
Michael S. Kramer, Richard M. Martin, Jonathan A C Sterne, Stanley H. Shapiro, Mourad Dahhou, Robert W. Platt

Bibliographic record

VenueBMJ · 2009
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsMcGill University
FundersEconomic and Social Research Council
KeywordsDouble jeopardyCluster (spacecraft)Cluster analysisCluster randomised controlled trialPsychologyMedicineArtificial intelligenceComputer scienceInternal medicineRandomized controlled trialPolitical scienceLaw

Abstract

fetched live from OpenAlex

Michael S Kramer and colleagues suggest that double clustering might explain the negative results of some cluster randomised trials and describe some strategies for avoiding the problem

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)0.519
metaresearch head score (Gemma)0.783
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.481
Threshold uncertainty score0.594

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5190.783
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0110.009
Bibliometrics0.0040.007
Science and technology studies0.0030.013
Scholarly communication0.0060.008
Open science0.0050.005
Research integrity0.0180.014
Insufficient payload (model declined to judge)0.0070.002

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 head0.053
GPT teacher head0.329
Teacher spread0.276 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations32
Published2009
Admission routes1
Has abstractyes

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