"Leading Professionals: Plurality, Process, and Power"
Bibliographic record
Abstract
This symposium brings together scholars of leadership and professional service firms to showcase an emerging body of research into how leadership is constructed and enacted in professional settings, and explore the impact this has on how these organizations develop. Leadership research has tended to neglect this distinctive context but it is clearly of generalizable concern, given the well-documented and damaging problems of leadership in professional settings such as investment banking. Conventional models of leadership are predicated on the assumption that leaders, by definition, must have followers, but in professional organizations traditional hierarchical dyadic relationships are replaced by more ambiguous and negotiated plural leadership relationships amongst peers. The distinctive challenges of leading professionals and attempting to change professional organizations derive from two interrelated organizational characteristics: extensive individual autonomy and contingent managerial authority. Clan control, i.e., behavior controlled through common values and traditions is the norm. As a result, leadership is inherently plural, and characterized by complex power dynamics and influencing processes. This symposium features five empirical studies in a range of professional settings (health care, law, accounting, investment banking, and management consulting), by scholars from the USA, Canada, the UK, France, and Sweden. Three contribute directly to theorization of plural leadership processes, by focusing on the co- construction of leadership authority, on novel configurations of dual leadership, and on leadership and change from a complexity perspective. Two examine the processes which influence how professionals are selected for leadership positions, and identify the unintended negative consequences of seemingly meritocratic systems. Challenges to Authority in Healthcare Systems: Enabling Change from a Complexity Leadership Lens (WITHDRAWN) Presenter: Mary Uhl-Bien; Texas Christian U. Co-leadership Dyads in Health Care Organizations: Bridging Professional and Managerial Logics? Presenter: Emilie M. Gibeau; HEC Montreal Presenter: Ann Langley; HEC Montréal Explaining the Gender Gap in Law Firm Leadership: Collaborative Networks as Resources Presenter: Heidi K. Gardner; Harvard U. Presenter: Madeline King; New York U. The Co-construction of Leadership among Professional Peers Presenter: Laura Empson; Cass Business School, City U. London Presenter: Johan Alvehus; Kristianstad U. The Criteria and Process of Promotion to Managing Director in Capital Markets Banking Presenter: Roxana Barbulescu; HEC Paris
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".