When Winning is Everything: The Relationship between Competitive Worldviews and Job Applicant Faking
Bibliographic record
Abstract
Job applicant faking, that is, consciously misrepresenting information during the selection process, is ubiquitous and is a threat to the usefulness of various selection tools. Understanding antecedents of faking is thus of utmost importance. Recent theories of faking highlight the central role of various forms of competition for understanding why faking occurs. Drawing on these theories, we suggest that the more applicants adhere to competitive worldviews (CWs), that is, the more they believe that the social world is a competitive, Darwinian‐type of struggle over scarce resources, the more likely they are to fake in employment interviews. We tested our hypothesis in three independent studies that were conducted in five different countries. Results show that CWs are strongly associated with faking, independently of job applicants’ cultural and economic context. More specifically, applicants’ CWs explain faking intentions and self‐reported past faking above and beyond the Dark Triad of personality (Study 1), competitiveness and the six facets of conscientiousness (Study 2). Also, when faking is measured using a response randomisation technique to control for social desirability, faking is more prevalent among applicants with strong vs. less strong CWs (Study 3). Taken together, this research demonstrates that competition is indeed strongly associated with undesirable applicant behaviors.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".