Positive Impression Management and Its Influence on the Revised NEO Personality Inventory: A Comparison of Analog and Differential Prevalence Group Designs.
Bibliographic record
Abstract
Participants (n = 22) completed the Revised NEO Personality Inventory (NEO PI-R) as part of an authentic job application. Protocols produced by this group were compared with "analog" participants (n = 23) who completed the NEO PI-R under standard instructions and again under instructions designed to mimic the test-taking scenario of the job applicants (the "fake-good" condition). Participants completing the NEO PI-R under fake-good instructions and the job applicants scored lower on the Neuroticism and higher on the Extraversion scales than did the participants responding under standard instructions. Analog participants in the fake-good condition scored higher on the Extraversion and lower on the Agreeableness scales than did the job applicants. These results suggest that outcomes from analog designs are generalizable to real-world samples where response dissimulation is probable.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".