"Gamified Cognitive Assessments in Selection: Validity, Discrimination and Applicant Reactions"
Bibliographic record
Abstract
Gamification is a fast growing trend in business practices, heralded for its ability to innovatively attract and select employees through motivated behaviour. Despite companies’ zealous adoption of gamification, notably the U.S. Army and L’Oréal’s recruitment and selection games, there is little scientific evidence on the effectiveness or validity of gamified processes in a selection context. The current study is the first of its kind to test the validity of a gamified selection process, specifically a gamified cognitive pre-employment assessment. Furthermore, this study examines the possibility that a gamified selection process might reduce or eliminate racial biases inherent in traditional cognitive ability measures. Mechanisms by which gamification might produce these benefits, including self-efficacy and motivation for the games are investigated. Finally, applicant reactions to the gamified selection process, including gender differences in applicant reactions are examined. Implications and future research directions are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".