Evaluation of warning strategies to reduce faking during military recruitment
Bibliographic record
Abstract
The applicant faking literature suggests that faking warnings - brief messages that dissuade applicants from faking - can reduce faking on personality tests by up to 50%. However, the efficacy of warnings may be limited by their atheoretical construction. Further, these threatening messages can cause applicants to feel negatively about the personality test, potentially reducing their validity during the selection process. We tried to improve the efficacy of faking warnings, while minimizing negative applicant reactions, by leveraging theory from the accountability and morality literatures. We tested three new faking warnings that emphasized short-term accountability, long-term accountability, and morality. To do so, we tested 466 military trainees undergoing basic training at the Canadian Armed Forces and asked them to engage in a selection simulation. We assigned groups of trainees to the different faking warning conditions and guided them through the simulation. We found that a faking warning emphasizing short-term accountability, which threatened to detect fakers by contacting references and using "internal integrity checks," reduced applicant faking. None of the other messages had any effect when compared to a no-warning control group.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".