The negotiation of sharing leadership in the context of professional hierarchy: Interactions on interprofessional teams
Bibliographic record
Abstract
While there is growing recognition of leadership as a collective phenomenon, the question of how leadership is shared in the context of hierarchical asymmetry has been neglected in the collective leadership literature. Our article addresses this gap by examining how sharing leadership is negotiated in team interactions that are steeped in asymmetry deriving from the professional hierarchy. Adopting a leadership-in-interaction approach, we draw on fine-grained analysis of observed interactions on interprofessional teams from two health care organizations to compare the discursive strategies used by professionals in a superior hierarchical position to the ones used by those in inferior positions to share leadership. These strategies are organized into a matrix of interactional moves that resist or enact the professional hierarchy. Empirical vignettes are provided to demonstrate how sharing leadership and hierarchical leadership can be co-present and even intertwined in an interaction. We show that leadership is shared (or not) as a result of how the professional hierarchy gets negotiated in interactions. More specifically, we conclude that the sharing of leadership in this context tends to occur prior to decision making, especially around problem formulation, if the interactional climate allows. Furthermore, it requires concrete effort: Those in superior positions of influence mindfully relax the hierarchy whereas those in inferior positions create moments of sharing leadership through resistance and struggle.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".