Principles of Leadership & Management
Bibliographic record
Abstract
This textbook is an open educational resource (OER) that represents just one stage of many journeys begun by others. Building on what earlier writers have compiled, predominantly on management, the book expands its focus to leadership principles with additional material on gender, diversity, inclusion, and Indigenous leadership. It also includes a Canadian perspective. There are case studies to help provide real world knowledge and the development of critical thinking and problem solving capabilities, as well as exercises, assignments and reflection for students to analyze and expand their own leadership skills. The text is directed toward students studying leadership from the viewpoint of multiple disciplines. Like the study and development of leadership itself, it is not yet complete, nor is it likely to ever be. The words of education expert Amy Collier apply well to the path of leadership and to this text: “Not-yetness is not satisfying every condition, not fully understanding something, not check-listing everything, not tidying everything, not trying to solve every problem…but creating space for emergence to take us to new and unpredictable places, to help us better understand the problems we are trying to solve” (Collier, A., 2015, para. 3). \nThere is great debate on the difference between leaders and managers. Beginning in Chapter 2, you will begin to see the terms leader and manager intermingled throughout the text. For some, this may be an affront to leadership (or management) principles, but the fact remains that good managers are leaders. Read more to support this concept by professor and researcher Henry Mintzberg. This text has been created for a course called Managing Leaders and Leadership in graduate certificate programs at Fanshawe College, and the content is geared not only toward students who are or will become formal leaders (with titles bestowed by an organization), but to those readers who may never fill an official leadership role. We all show regular everyday leadership as we go about our day-to-day lives of working, socializing, parenting, etc. – whether we are aware of it or not. The text addresses myriad leadership concepts to empower the emergence of your own personal leadership, and to assist you in allowing others to do the same. I wish you well as your authentic leadership emerges, and as you enable that emergence in others. May you find joy in the journey – both personally and professionally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.022 |
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".