Bibliographic record
Abstract
ABSTRACT: This article contends that Hannah Arendt’s writing can add value to current discussions on responsible leadership. Specifically, considering responsibility through an Arendtian lens offers insights that deepen our understanding of the interconnections among leadership, responsibility, and ethical action. Turning to Arendt can, therefore, increase our grasp of the complexities of leading responsibly. She shows how acting responsibly requires not only ethical forethought but also a willingness to judge for ourselves. Her emphasis on judgment enriches discussions on responsible leadership, encouraging us to think more deeply about what it might mean to act responsibly, and how such action connects with ethics. Examples of irresponsible action are explored as they concern individual and collective judgment in particular political and corporate contexts. Thus, it is by engaging with the messy realities of everyday life that an Arendtian turn can help us rethink leadership, ethics, and responsibility in new and productive ways.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".