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Record W4235644751 · doi:10.31219/osf.io/r5c28

The vanity construct re-examined: LeBel's vanity scale

2017· preprint· en· W4235644751 on OpenAlexaff
Etienne P. LeBel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsScale (ratio)ConceptualizationPsychologyReliability (semiconductor)Discriminant validityConstruct validityConstruct (python library)Convergent validitySocial psychologyPsychometricsInternal consistencyClinical psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A new vanity scale was developed according to a broader conceptualization, distinguishing between physical and intellectual vanity. The new scale was psychometrically validated using a total of 339 participants. Three studies were carried out using undergraduate students and family and friends. Validation procedures included assessing the convergent and discriminant validity of our scale using the Balanced Inventory of Desirable Responding (BIDR) and the International Personality Item Pool (IPIP). Internal reliability and factor analytic procedures were also performed on the scale data. Cumulative results of the three studies support the psychometric properties of the new scale. The final 22-item version of the scale showed high internal reliability and excellent factor structure. It is concluded that the scale may potentially be used for general purpose research to identify vain individuals.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.087
GPT teacher head0.389
Teacher spread0.303 · 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 designObservational
Domainnot available
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

Citations2
Published2017
Admission routes1
Has abstractyes

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