Co-Leadership in the Arts and Culture
Bibliographic record
Abstract
This book is about co-leadership: A leadership practice and structure often found in arts organizations that consist of two or three executives who bridge the art and business divide at the top. Many practitioners recognize this phenomenon but the research on this topic is limited and dispersed. This book assembles a coherent overview and presents new insights of the field. While co-leadership is well institutionalized in the West, it is also criticized for management’s constraint of artistic autonomy and for its pluralism that dilutes leadership clarity. However, co-leadership also personifies the strategic objectives of art, audiences, organization, and community, by addressing plural logics – navigating the demands of artistic vision and organizational stability. It is an integrating solution. The authors investigate its specifics in the arts, including global practice and its interdisciplinary nature. The theoretical frame of plural leadership supports their empirical explorations of the dynamics within the co-leadership relationship and with organizational stakeholders. Data includes the voices of co-leaders, artists, staff, and board members from arts organizations in Canada and Norway. Their abductive reflection generates a stimulating research experience. By viewing co-leadership in action, not as a study of static theories, the book will appeal not only to students and researchers but also resonate with practitioners in arts and cultural management and assist them to work with co-leadership and to manage its tensions. Chapter 4 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".