Sustainability Leadership and Ethical Perspectives
Bibliographic record
Abstract
The failures of leaders in the 21st century have reached crisis proportions, as evidenced by the alarming trends of “Quiet Quitting” and “The Great Resignation” (Jamali & Caldwell, 2023). Beck and Harter (2023) reported that 82% of people promoted to positions of leadership were the wrong individuals and 58% of employees in a 2018 survey indicated that they would rather trust a stranger than their boss (Damron, 2018). For organizations to reverse this trend of ineffective leadership, they must develop a sustainable leadership approach that incorporates true ethical principles (Hasan, 2022).The purpose of this paper is to address the need for “Sustainability Leadership” in modern organizations and to emphasize the importance of ethical leadership (Stouten, Van Dijke, & De Cremer, 2012). We begin this paper by defining Sustainability Leadership (SL) and emphasizing its importance as a leadership framework. After defining SL, we then identify how SL meshes with seven different leadership perspectives. We suggest eight propositions that leaders and organizations can test related to each perspective’s contribution to SL. We conclude the paper by identifying four contributions of this paper for leaders and organizations
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".