A Test of Expectancy Theory and Demographic Characteristics as Predictors of Faking and Honesty in Employment Interviews
Bibliographic record
Abstract
Job applicants vary in the extent to which they fake or stay honest in employment interviews, yet the contextual and demographic factors underlying these behaviors are unclear. To help answer this question, we drew on Ellingson and McFarland’s (2011) framework of faking based in valence-instrumentality-expectancy theory. Study 1 collected normative data and established baseline distributions for instrumentality-expectancy beliefs from a Canadian municipality. Results indicated that most respondents had low levels of instrumentality-expectancy beliefs for faking, but high levels for honesty. Moreover, income, education, and age were antecedents of instrumentality-expectancy beliefs. Study 2 extended these findings with a United States sample and sought to determine if they could be explained by individual differences. Results demonstrated that financial insecurity predicted instrumentality of faking, whereas age predicted expectancy of faking. Finally, valence-instrumentality-expectancy beliefs were all predictors of self-reported faking in a past interview.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".