Leadership Special Issue: Do we need Authentic Leadership? Interrogating authenticity in a new world order
Bibliographic record
Abstract
Authentic Leadership (AL) has been claimed as the ‘root construct’ (Avolio and Gardner, 2005) for other forms of ‘aspirational’ leadership with underpinnings in positive psychology. It has also been explicitly positioned as a response to the ‘ethical corporate meltdown’ (May et al., 2003: 247) said to have resulted from previous forms of leadership. Yet it has struggled to live up to its acknowledged functionalist and instrumentalist aims. At the same time, AL has proved resistant to important philosophical challenges seeking to problematize the nature of the ‘true self’ and draw attention to the complexities of enacting authenticity in the daily practice of leadership. These ambitious claims and unaddressed issues are at the heart of this special issue’s enquiry as to whether AL is fit for purpose as a driver of leadership theory and practice in the current world order, and its call for more critical attention to be paid to the notion of authenticity in leadership. The contributions to this special issue blend traditional, empirical papers with invited ‘Leading Questions’ thought pieces to offer a fundamental interrogation of authenticity at the same time as achieving a balance of perspectives.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".