When Everyone and No One is a Leader: Constructing individual leadership identities while sustaining an organizational narrative of collective leadership
Bibliographic record
Abstract
Our paper investigates the dynamic interplay of narratives of individual and collective leadership within a professional service firm, where an organizational narrative of collective leadership prevails. We explain how it is possible for ‘everyone’ to claim a leadership identity for themselves while simultaneously granting a leadership identity to the collective. We identify multiple leadership archetypes embedded in individuals’ identity narratives, representing their differing senses of themselves as leaders and their alignment with the organizational narrative of collective leadership. These archetypes are mutually constitutive, representing centripetal and centrifugal tendencies in relation to the organizational narrative of collective leadership. We show how individuals committed to collective leadership nevertheless construct an individual leader (the Avatar identity archetype) to embody the collective on their behalf, and this enables them to grant leadership to the collective in the abstract. We emphasize the persistent sacralization of leadership in individual and organizational narratives, even in avowedly collectivist contexts, and the value of narrative-based perspectives in highlighting practitioners’ ability to navigate and accommodate the messy coexistence of collective and individual leadership. Our study shows the importance of integrating dialectically the individual and collective dimensions of leadership, emphasizing the mutually constitutive nature of individual and collective leadership narratives.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".