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Record W4254974440 · doi:10.31234/osf.io/v796c

Why Are Self-Report and Behavioral Measures Weakly Correlated?

2020· preprint· en· W4254974440 on OpenAlexaff
Junhua Dang, Kevin M. King, Michael Inzlicht

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConstruct (python library)Reliability (semiconductor)PsychologyConstruct validityCognitive psychologyEconometricsSocial psychologyComputer sciencePsychometricsClinical psychologyMathematicsPhysics

Abstract

fetched live from OpenAlex

Accumulating evidence indicates weak correlations between self-report and behavioral measures of the same construct. We suggest these weak correlations result from poor reliability of many behavioral measures and distinct response processes involved in the two measurement types. We also describe how researchers can benefit from appropriate use of these measures

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)0.161
metaresearch head score (Gemma)0.517
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.851

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1610.517
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.005
Science and technology studies0.0010.008
Scholarly communication0.0060.005
Open science0.0030.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.146
GPT teacher head0.425
Teacher spread0.279 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreEmpirical

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

Citations41
Published2020
Admission routes1
Has abstractyes

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