Construct‐based approach to developing a short, personality‐based measure of integrity
Bibliographic record
Abstract
Covert integrity measures are thought to draw from the Big Five dimensions of conscientiousness, agreeableness, and emotional stability. Using a construct‐based approach, we had subject matter experts identify items from a Big Five personality measure, the Trait Self‐Descriptive Personality Inventory that reflected an operational definition of integrity. The resulting 10 items exhibited a three‐factor structure that corresponded to the three Big five dimensions associated with integrity. Study 1 used primary (N = 388) and archival (N = 429) data sets collected from Canadian Armed Forces recruits to establish the construct validity of the new test. With respect to convergent and discriminant validity, the Integrity scale was related to the Honesty–Humility scale of the HEXACO‐PI and was unrelated to organizational commitment. Hierarchical regression analyses provided evidence that the integrity scale predicted counterproductive work behavior and job performance over and above the Big Five. Study 2 replicated the results of Study 1 using a civilian sample (N = 200). The Integrity scale was related to the Hogan Reliability Index but not to the General Health Questionnaire. It predicted work engagement over and above the Big Five. We also tested the proposition that integrity is a second‐order factor based on conscientiousness, agreeableness, and emotional stability. Structural equation models in both studies confirmed that proposition. We discuss the implications of our results for both theory and practice.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".