Worldviews and leadership: Thinking and acting the bigger pictures
Bibliographic record
Abstract
Abstract Leadership is about ideas and actions. Put simply, it is about implementing new ideas into creative actions to achieve desired results. Doing so, however, is far from simple. We know leadership requires considerable skills and abilities. It requires knowledge and insight—about one's organization or entity, its people, goals, strengths and market niche. Yet, something more is needed. Leadership also requires a kind of awareness beyond the immediate, an awareness of the larger pictures—of paradigms that direct us, beliefs that sustain us, values that guide us and principles that motivate us, our worldviews. This article will, first, briefly examine how the concept of worldviews is used in leadership study and the contexts in which it arises. Second, it will critically look at worldviews, recognizing that they are not always coherent and that our belief systems are often fragmented and incomplete. Third, it will argue for the relevance of the concept worldview in leadership study as a way to explore various visions of life and ways of life that may be helpful in overcoming the challenges we face today. Fourth, it will examine how national and global issues impact worldview construction, especially among the millennial generation. Our conclusions set some directions for leadership action in light of worldview issues.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".