Bibliographic record
Abstract
The Problem Authentic leadership (AL) focuses on enhancing the capabilities and capacities of leaders. Instead of focusing on the leader, this article considers the ethical challenges of fostering authenticity among employees. One such challenge is that marginalized groups may feel unable to be true to their values, a key premise of authentic leadership. To foster workplaces where marginalized groups feel able to be true to themselves, we must consider the cultural and structural barriers that can negatively affect people’s ability to express themselves in the workplace. Hence, when considering the merits of AL, it is important for human resource development (HRD) professionals to consider what occurs when employees do not fit institutional norms. The Solution Creating the conditions for a relational authentic approach to AL that allows authentic otherness to flourish is an important and complex ethical task, one that HRD scholars and practitioners are uniquely placed to encourage. The Stakeholders HRD scholars and practitioners interested in creating the conditions that encourage the flourishing of authentic otherness among employees.
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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.058 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".