Leadership as the Hybrid Production of Presence(s)
Bibliographic record
Abstract
Leadership scholars and lay actors often attribute a certain presence to great leaders in describing a commanding style or a charismatic personality. However, leadership presence and its mirror concept, absence, have been difficult concepts for researchers to study. This article proposes to redress this short coming using actor-network theory (ANT). In ANT, the focus is on human and nonhuman agents, their hybrid forms, networked socialaction, and macro acting, the latter of which enables leaders or followers to speak on behalf of their organizations. Together with ANT’s emphasis on the role of narrative, this approach directs analysts to the situated construction of actor networks in which leadership presence or absence is attributed. An emphasis on discourse also shows how various actants are imbued with meaning, enabling analysts to unravel networks andflows of power associated with leadership presence/absence. Leadership discourses involving charismatic/transformational leadership are considered as well as the disaster management networking associated with two US Governors, Arnold Schwarzenegger and Kathleen Blanco, for their respective handling of the California wildfires and hurricane Katrina.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".