MétaCan
Menu
Back to cohort

Design Effects

2014· other· en· W4246135378 on OpenAlexaff
Neil Klar, Allan Donner

Bibliographic record

VenueWiley StatsRef: Statistics Reference Online · 2014
Typeother
Languageen
FieldDecision Sciences
TopicOptimal Experimental Design Methods
Canadian institutionsWestern UniversityCancer Care Ontario
Fundersnot available
KeywordsRelevance (law)Context (archaeology)Sample size determinationCluster samplingSampling designSampling (signal processing)Sample (material)Computer scienceCluster (spacecraft)Management scienceStatisticsMathematicsGeographySociologyEngineeringPolitical sciencePhysicsProgramming language

Abstract

fetched live from OpenAlex

Abstract The purpose of this article is to review the concept of design effect as originally defined in the context of survey sampling, but also to demonstrate its strong relevance to broader areas of scientific interest. A worked example is provided illustrating its application of this concept to the planning of sample size requirements for a school‐based cluster randomization trial.

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)0.081
metaresearch head score (Gemma)0.188
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: Methods · Consensus signal: Methods
Teacher disagreement score0.176
Threshold uncertainty score0.589

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0810.188
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0030.002
Science and technology studies0.0020.003
Scholarly communication0.0040.004
Open science0.0030.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.1760.025

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 head0.188
GPT teacher head0.461
Teacher spread0.273 · 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
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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueWiley StatsRef: Statistics Reference OnlineSame topicOptimal Experimental Design MethodsFrench-language works237,207