In search of the pseudo-transformational leader: A person-centered approach
Bibliographic record
Abstract
Unlike truly transformational leaders who inspire and empower others to achieve a collective good, pseudo-transformational leaders (pseudo-TL) do so for their own gain. Christie et al. (2011) found preliminary support for a behavioral model proposing that pseudo-TL display the same inspirational motivation as authentic-transformational leaders (authentic-TL), but fail to display other typical transformational behaviors. In this study, we take a person-centered approach to replicate and extend Christie et al.’s (2011) research. We included leader self-interest as an attributed quality of pseudo-TL along with the transformational leadership facets. In two studies (an experimental simulation, N = 154; a survey study, N = 292), we found that while both displayed inspirational motivation, compared to authentic-TL, pseudo-TL were rated lower on intellectual stimulation, individualized consideration and idealized influence. They were perceived as more self-interested, less trustworthy, and evoked lower levels of trust. We identified five distinct profiles in Study 2, suggesting the possible need to expand the concept of pseudo-TL beyond that proposed by Christie et al. (2011).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".