The Development and Initial Validation of the Self-Presentational Defensiveness Scale
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".