The Me You (Don’t) See: How Leaders Filter Intrapersonal Information at Work
Bibliographic record
Abstract
As the expectations to “be yourself at work” rise, leaders must determine which self they reveal in what settings. To explore how leaders balance their self-identity in steady contexts, as well as crisis situations, we adopt an exploratory and inductive qualitative approach to understand how they filter their intrapersonal experiences and why. We draw on 65 in-depth interviews with leaders in a variety of industries and occupations collected at two points in time. Integrating signaling theory, we advance a model that accounts for the process through which leaders filter intrapersonal information to their followers. We find that in a steady state, leaders aim to share information that is consistent with their leader identity in order to send cues to their followers that signal desired messages, such as competence and safety, which are necessary to lead effectively; however, in crisis, these same signals are cued differently. Our work extends research on leader identity, signaling theory, and authentic leadership by depicting a process that explains how leaders selectively filter their intrapersonal thoughts, feelings, and behaviors in order to match the contextual demands of the situation and lead effectively.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".