Authentic Leadership in HRD—Identity Matters! Critical Explorations on Leading Authentically
Bibliographic record
Abstract
The Problem Authentic leadership (AL) has been viewed as an attractive leadership model to combat destructive forms of leadership. On a simple level, it is difficult to argue against authenticity when leading and developing leaders. However, on a deeper level, many scholars have challenged the ideas supporting authentic leadership to highlight the model’s theoretical assumptions and implicit values. Of the critiques, one of the most relevant challenges for HRD (Human Resource Development) is the critique based on identity because this critique aligns with HRD’s focus on diversity and inclusion. The problem is that HRD researchers and practitioners need to understand more about how authentic leadership, as described typically in scholarly and practitioner journals, homogenizes the workplace and discounts diverse ways of being authentic. The Solution The articles in this Special Issue offer a variety of different perspectives on the connection between authentic leadership and identity to make transparent the hidden assumptions, power dynamics, and contextual forces at play. When these unexamined and implicit factors are considered, HRD scholars and practitioners will be in a better position to promote diversity and inclusion in the workplace, as well as in teaching, research, and service. The Stakeholders Researchers and practitioners interested in authentic leadership, diversity and inclusion, and power.
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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 | 0.038 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.083 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.015 |
| 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".