The (non)distribution of leadership roles: Considering leadership practices and configurations
Bibliographic record
Abstract
This article draws on distributed leadership and leadership-as-practice perspectives to report on a comparative case analysis of leadership configurations. The context of acquisitions is used in the study. Attention is given to the practices of members of the two leadership teams – one from each of the acquiring and acquired organizations – as they attempted to integrate their practices and redistribute leadership roles. The findings show that, despite expectations that distributed leadership would be achieved, the emergent configurations varied across the firms and consisted of distributed leadership, distributed leaderlessness, overlapping leadership and non-distributed leadership. These configurations were underpinned by members’ framings, relational practices and (non)exercise of agency. The article contributes to the leadership literature by proposing the notions of leadership deficits and leadership surpluses in configurations, by exploring how ambiguous leadership spaces are constructed, and by providing evidence of leadership models that vary in terms of conflict tractability. The study uncovers the limits of distributed leadership and shows that not all is well with distributed leadership models. The article also contributes to a broader understanding – than has been achieved through extant literature – of various potential leadership configurations that can emerge in the case of acquisitions and beyond.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".