Bibliographic record
Abstract
The purpose of this sourcebook is to explore the intersection of adult learning and collaborative leadership.With the notable exception of Clover, Butterwick, and Collins' (2016) compilation titled Women, Adult Education, and Leadership in Canada, little has been written about the possibilities for learning when adults engage in collaborative approaches to leadership.Drawing from scholarship related both to collaborative leadership (e.g., Chrislip, 2002;Chrislip & Larson, 1994;Crevani, Lindgren, & Packendorff, 2007) and to adult education and learning, this sourcebook weaves theory with practice by showcasing real-life examples of collaborative leadership.The term leadership is, as Grint (2005) suggested, an essentially contested concept, with a plethora of literature written in its name yet very little agreement about what leadership means.Although the traditional idea of an individual, charismatic, often male, heroic leader (Carlyle, 1841) remains a popular stereotype, current scholarship and practice tell us that leadership takes many forms, with an increasing focus on collaboration (Raelin, 2016).By attending to the adult learning that takes place through more collaborative approaches to leadership, this collection draws upon scholars who understand leadership as emergent (Scharmer, 2007), distributed (Bolden, Petrov, & Gosling, 2009), democratic (Shields, 2009), transformational (Norris, Barnett, Basom & Yerkes, 2002), and compassionate in uncertain times (Wheatley, 2006(Wheatley, , 2005)).This sourcebook looks beyond position-based individual leadership to capture how people learn through the diverse actions, processes, and strategies collaborative leaders employ to bring about change.The authors in this sourcebook are seeking ways to understand not only how leadership is enacted among individuals but also how it is expressed in collective ways of thinking, doing, being, knowing, and learning.Collaborative leadership settings offer insights into a range of topics of continued relevance to adult educators.For example, when groups of adults take initiative to learn what is needed to accomplish the task at hand, this can offer insight into self-directed learning (Knowles, 1975).When this collaborative learning becomes a natural part of one' s leadership practice and way of being in the world, this can offer insights into lifelong learning (Sutherland & Crowther, 2006) or informal and incidental learning (Foley, 1999).The authors in this sourcebook offer a depth of understanding about learning in collaborative leadership settings.Chapter 1 begins with Virginia McKendry' s use of ensemble leadership and generative learning theories to make sense of an Indigenous speaker series formed to foster intercultural partnerships at a Canadian university.McKendry argues that ensemble leadership is a key element in designing the generative learning adult learners need in an era of ambiguity.
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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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.218 | 0.168 |
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".