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Record W2582841787 · doi:10.1177/0146621615597894

faoutlier

2015· article· en· W2582841787 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

VenueApplied Psychological Measurement · 2015
Typearticle
Languageen
FieldMathematics
TopicAdvanced Statistical Methods and Models
Canadian institutionsYork University
Fundersnot available
KeywordsExploratory factor analysisComputer scienceConfirmatory factor analysisRegression analysisSoftwareExtant taxonFactor (programming language)StatisticsStatistical analysisRegressionArtificial intelligenceMachine learningMathematicsStructural equation modelingProgramming languageBiology

Abstract

fetched live from OpenAlex

Like any statistical modeling procedure, results from both exploratory and confirmatory factor analysis are vulnerable to disproportionate influence from unusual or outlying cases.Because the common factor model is a type of multiple regression model, concepts from regression case diagnostics can be applied to factor analysis.For instance, Pek and MacCallum (2011) and Flora, LaBrish, and Chalmers (2012) present several useful techniques for detecting influential cases and demonstrate their utility in applied settings.However, extant factor analysis software packages generally do not implement these important case diagnostic methods; therefore, specialized software is required.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.642
GPT teacher head0.502
Teacher spread0.140 · 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