Bibliographic record
Abstract
Drawing on several theoretical traditions in the social sciences, we offer a theory of the social facilitation of charismatic leadership by introducing the concept of squires. Squires are key followers who serve four social facilitation functions: liberating and legitimizing, modeling, buffering, and interpreting and translating. Liberating and legitimizing builds on social conformity research. Modeling is based in the social learning and social influence literatures. Buffering, and interpreting and translating, draw on insights from the psychology of power and organizational theory. These functions help resolve two central charismatic leadership paradoxes: (a) the need to be different from followers, though followers prefer to be led by leaders who are like them, and (b) the need to be personally inspiring to followers while being socially distant from them. In specifying squires’ functions, we also address three weaknesses in conceptions of followership and contribute to understandings of how charismatic leadership emerges, works, and endures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.105 | 0.029 |
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".