Integrating forced choice with rapid response measurement
Bibliographic record
Abstract
Abstract Rapid Response Measurement (RRM) presents stimuli in rapid succession, which has been shown to effectively limit applicant faking. This study validates a novel measure integrating forced choice item pairs with RRM. Three assessment formats were evaluated for their susceptibility to faking, construct and criterion‐related validity, and potential adverse impact: Single Stimulus (SS), traditional Forced Choice (traditional FC) and the new Rapid Response Forced Choice (RRFC). Faking susceptibility was highest for SS, followed by traditional FC, with RRFC exhibiting the greatest resistance. Both FC and RRFC demonstrated enhanced fake resistance at low selection ratios. Notably, our findings suggest that RRFC maintains criterion‐related validity even in simulated applicant conditions where maximal faking is expected. Although construct validity degraded in the SS format, it was preserved in both the FC and RRFC formats. Respondents completed the RRFC approximately three times faster on average and showed the least potential for adverse impact compared to the other two formats. Given the speeded nature of the RRFC, it may be uniquely capable of limiting AI‐based cheating. Future directions are discussed.
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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 | 0.024 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".