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Record W3203362329 · doi:10.1080/08982112.2021.1938118

Identifying dominant causes using leveraged study designs

2021· article· en· W3203362329 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuality Engineering · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExploitComputer scienceProcess (computing)Plan (archaeology)Variation (astronomy)Data miningComputer security

Abstract

fetched live from OpenAlex

Dorian Shainin’s group comparison procedure is a way to find the dominant cause(s) of variation in a process, based on an investigation plan that exploits the idea of leveraging. In this paper, we study whether Shainin’s procedure is sound. Our main finding is that Shainin’s procedure is unreliable in identifying the dominant cause, and in addition, the procedure is inefficient. We propose a new efficient and sound analysis procedure based on the method of maximum likelihood. A critical evaluation shows our proposed method is superior. We also provide a tangible example and compare both methods’ outcomes.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.505
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.550
GPT teacher head0.536
Teacher spread0.014 · 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