Transformational Leadership Plans
Bibliographic record
Abstract
Higher education institutions are aware of the need to incorporate learning-driven assessment artifacts into theirdoctoral programs to ensure successful leadership development. However, in attempting to integrate learning intostudents’ future performance, it appears that there is no general agreement upon the most effective assignments. Theplethora of leadership tasks makes it difficult for scholars and instructors to guarantee that doctoral learners will usetheir leadership skills in their current or future workplaces. One way to incorporate current learning into professionalperformance is to translate course learning into personal leadership development plans. This article describes ashort-term transformational leadership development plan rooted in the Scholar/Practitioner/ Leader Model,University of Phoenix, U.S.A., educational model. The plan is a culminating learning task of a TransformationalLeadership and Innovation course. Upon completion of this course, doctoral learners would be prepared toincorporate the principles of transformational leadership into their leadership plans when completing them in specificacademic contexts. The plan proposed defines a strategy for maximizing leadership effectiveness and spells outlearning from doctoral courses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".