Self–Other Rating Accuracy and Leadership Emergence: Does Rating Accuracy Influence Who Emerges as a Leader?
Bibliographic record
Abstract
The current study investigated how individual differences in self–other rating agreement (SOA) were related to leadership emergence. A sample of 4,524 students from MBA programs in the United States and Canada completed a leaderless group task as part of an assessment center. The results revealed that emergence varied by SOA, with underraters exhibiting the highest levels of emergence, followed by self‐aware (i.e., accurate), and then overraters. One of the intriguing results is that underraters were more likely to display emergent behaviors than accurate raters, raising questions about the widely held belief regarding the use of accurate self‐assessments as an indicator of leadership effectiveness. Overall, the results indicate that SOA is an antecedent of leadership emergence behaviors. While prior research has examined the effect of SOA on performance, commitment, and leadership perceptions, the study contributes to the literature by examining whether SOA influences actual emergence behavior.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".