Leader Character: Theory to Practice
Bibliographic record
Abstract
To ensure that the theory of leader character is developed through a performativity lens, we seek to bridge the theory - practice gap by asking: What are the essential elements of leader character in organizational contexts? In responding to this research question we make two core contributions. First, our research serves to bring leader character into mainstream management theory and practice through a performativity epistemology. While character’s history resides in philosophy and ethics, and more recently psychology, co-creating the understanding of leader character with practicing leaders not only bridges the theory – practice gap that informs practice, but also helps to inform future research seeking to apply leader character in organizational contexts. Second, we bridge the divide that exists between descriptive and normative leadership theories. We describe and develop the underlying theory of leader character through a three phase, multi-method approach involving close to 2,000 leaders in three organizations. We present our findings and the implications for a framework of leader character that can be utilized by both leadership researchers and practitioners, concluding with possible next steps in the research agenda.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".