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Record W2180200726 · doi:10.1002/sim.6808

2 × 2 Tables: a note on Campbell's recommendation

2015· article· en· W2180200726 on OpenAlexaff
Frank Busing, Bruce Weaver, Sacha Dubois

Bibliographic record

VenueStatistics in Medicine · 2015
Typearticle
Languageen
FieldMathematics
TopicAdvanced Statistical Methods and Models
Canadian institutionsEssar Steel Algoma (Canada)St. Joseph's Care GroupNOSM UniversityLakehead University
Fundersnot available
KeywordsStatisticPearson's chi-squared testStatisticsChi-square testPearson product-moment correlation coefficientMathematicsMean squared errorTest statisticStatistical hypothesis testing

Abstract

fetched live from OpenAlex

For 2 × 2 tables, Egon Pearson's N - 1 chi-squared statistic is theoretically more sound than Karl Pearson's chi-squared statistic, and provides more accurate p values. Moreover, Egon Pearson's N - 1 chi-squared statistic is equal to the Mantel-Haenszel chi-squared statistic for a single 2 × 2 table, and as such, is often available in statistical software packages like SPSS, SAS, Stata, or R, which facilitates compliance with Ian Campbell's recommendations.

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)0.221
metaresearch head score (Gemma)0.719
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.961

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2210.719
Meta-epidemiology (narrow)0.0020.005
Meta-epidemiology (broad)0.0060.008
Bibliometrics0.0130.019
Science and technology studies0.0040.011
Scholarly communication0.0130.014
Open science0.0170.007
Research integrity0.0250.043
Insufficient payload (model declined to judge)0.0650.071

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 head0.206
GPT teacher head0.504
Teacher spread0.298 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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