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Record W1967757406 · doi:10.1198/tast.2009.0028

Design Effect Caveats

2009· article· en· W1967757406 on OpenAlexaff
Jack Gambino

Bibliographic record

VenueThe American Statistician · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsSimple (philosophy)Variance (accounting)StatisticsComputer scienceDesign of experimentsEconometricsMathematicsEconomicsEpistemology

Abstract

fetched live from OpenAlex

Design effects typically are used either to compare the efficiency of survey designs or to make it possible to use simple formulas to compute the variance of estimates from complex surveys. Here we discuss some of the pitfalls that may be encountered in practice when a design effect is computed incorrectly or the wrong design effect is used.

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)0.377
metaresearch head score (Gemma)0.799
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: Methods
Teacher disagreement score0.623
Threshold uncertainty score0.769

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3770.799
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0050.008
Bibliometrics0.0060.010
Science and technology studies0.0040.013
Scholarly communication0.0070.010
Open science0.0130.006
Research integrity0.0150.026
Insufficient payload (model declined to judge)0.0400.007

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 head0.033
GPT teacher head0.268
Teacher spread0.234 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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