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Record W4414081053 · doi:10.1080/00223891.2025.2542259

The Development and Initial Validation of the Self-Presentational Defensiveness Scale

2025· article· en· W4414081053 on OpenAlexafffund
Chris Sciberas, Marc A. Fournier

Bibliographic record

VenueJournal of Personality Assessment · 2025
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council
KeywordsDiscriminant validityObservabilityScale (ratio)PsychometricsReliability (semiconductor)Measurement invarianceConvergent validityTest validity

Abstract

fetched live from OpenAlex

Informed by psychoanalytic, humanistic, and cybernetic perspectives on defensive functioning, the present work established the psychometric structure and initial validation of the 10-item Self-Presentational Defensiveness Scale (SPDS). Across four studies (total N = 1,634), we assessed the item-level observability of the initial 20-item SPDS (Study 1), explored the psychometric structure of the initial SPDS in two separate samples (Studies 2 and 3), and established the psychometric properties of the final 10-item SPDS (Study 4), along with preliminary evidence of convergent and discriminant validity. The SPDS demonstrated (a) item content that was rated as more observable compared to other commonly used measures of defensive functioning, (b) a robust substantive self-presentational defensiveness factor, (c) measurement invariance across gender (i.e., male and female) and measurement type (i.e., self and informant ratings), (d) substantial self-other agreement (i.e., r = .42), and (e) appropriate correlations with theoretically related constructs (e.g., neuroticism). These results demonstrate the reliability and initial validity of the SPDS and suggest that self-presentational defensiveness reflects a lack of personal accountability when confronted with negative self-relevant stimuli.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.055
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.377
Teacher spread0.346 · 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 teacher head, 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

Citations1
Published2025
Admission routes2
Has abstractyes

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