Economic Predictors of Differences in Interview Faking Between Countries: Economic Inequality Matters, Not the State of Economy
Bibliographic record
Abstract
Many companies recruit employees from different parts of the globe, and faking behavior by potential employees is a ubiquitous phenomenon. It seems that applicants from some countries are more prone to faking compared to others, but the reasons for these differences are largely unexplored. This study relates country‐level economic variables to faking behavior in hiring processes. In a cross‐national study across 20 countries, participants ( N = 3,839) reported their faking behavior in their last job interview. This study used the random response technique (RRT) to ensure participants’ anonymity and to foster honest answers regarding faking behavior. Results indicate that general economic indicators (gross domestic product per capita [GDP] and unemployment rate) show negligible correlations with faking across the countries, whereas economic inequality is positively related to the extent of applicant faking to a substantial extent. These findings imply that people are sensitive to inequality within countries and that inequality relates to faking, because inequality might actuate other psychological processes (e.g., envy) which in turn increase the probability for unethical behavior in many forms.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".