Bibliographic record
Abstract
Purpose Many authors have called for a more humane and effective type of leadership. This article seeks to propose a research program on the content and process of integral leadership. This type of leadership has been exemplified by leaders known for their ethical and spiritual maturity, such as Nelson Mandela, the Dalai Lama, Mother Teresa, Eleanor Roosevelt, Martin Luther King, Mohandas Gandhi and Rachel Carson, among others, and by many men and women who have not achieved fame. Design/methodology/approach As this research requires a multi‐disciplinary, multi‐level and developmental approach, Ken Wilber's integral model is described and used as a frame for the research program, going beyond the limitations of current leadership inquiry. Findings After having presented both the critics offered on leadership research and the tenets of the integral model, the article proposes a research program articulated by the analysis of individual cases of this leadership pattern and the collective analysis of these cases. Further, it adopts a micro, meso and macro perspective through the use of three methodologies: interpretative biography, institutional analysis and historical inquiry. Originality/value This research program contributes to a developmental theory of leadership. Researchers will find in this paper an innovative and sounded research program which can generate results on both the practice and development of a type of leadership we badly need.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".