A Registered Report to Disentangle the Effects of Frame of Reference and Faking in the Personnel‐Selection Scenario Paradigm
Bibliographic record
Abstract
ABSTRACT In laboratory faking research, participants are often instructed to respond honestly (generic instructions [GIs], control condition) or to fake (personnel‐selection scenario [PSS], faking condition). Considering the research on instruction‐level contextualization, a PSS might not only motivate participants to fake but might also promote the adoption of a work frame of reference (FOR). Thus, differences in responses between faking and control conditions could partly result from FOR effects. (Full) item‐level contextualization can also be used to promote the adoption of a work FOR, and the adoption through this route is stronger than through instruction manipulation. We combined the two approaches to disentangle FOR and faking, conducted a 4‐wave longitudinal study with a 2 (instructions: GIs vs. PSS) × 2 (full item‐level work contextualization absent vs. present) repeated‐measures design ( N = 309), and compared the effects of these conditions on three HEXACO‐PI‐R scales (Conscientiousness, Emotionality, Honesty‐Humility). Irrespective of the investigated personality trait, the ANOVAs revealed significant main effects. As expected, compared with GIs, the PSS increased the adoption of a work FOR, and the effects were smaller than the effects of full item‐level work contextualization present (vs. absent). Also, as expected, the PSS (vs. GIs) and full item‐level work contextualization present (vs. absent) changed participants' scale mean scores. However, importantly, there were no interaction effects. Exploratory mediation analyses indicated direct rather than indirect (mediator: adoption of a work FOR) effects of instructions on participants' scale mean scores. In conclusion, the internal validity of faking research is not threatened by confounding FOR effects.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".