The burden of being atypical: The impact of (A)typicality in leader profiles on organizational reputation
Bibliographic record
Abstract
Despite growing interest in appointing top leaders with atypical biographical profiles, many organizations follow typical expectations of what a leader’s profile looks like and avoid deviations from such expectations. This article aims to answer why such changes to leadership atypicality can be difficult by examining a major disadvantage of atypical leader profiles—organizational reputational penalties. Drawing on institutional theory and leadership categorization theory, we propose that atypical components in a leader’s profile are met with greater skepticism and scrutiny of leadership capability from external stakeholders, thereby leading to reputation losses. We examine this argument by developing and testing hypotheses on the reputational impact of atypicality in deans’ profiles in American law schools from 1998 to 2016. Our results show that atypical attributes of a leader’s profile are negatively associated with organizational reputation across a broad spectrum of deans’ key profile attributes, including their career path, education credentials, and gender minorities. We discuss the implications of these findings for the study of organizational atypicality and reputation.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".