The Leader as Catalyst: On Leadership and the Mechanics of Institutional Change
Bibliographic record
Abstract
Individual leaders have been central to the transformation of organizations, political institutions and many instances of social and economic reform. In this paper we take a first step towards analyzing the role of leadership to ask: when and how does a leader engineer change? We show that while underlying structural conditions and institutions are important, there is an independent firstorder role for individual agency in bringing about change and thus transforming the institutions. We emphasize the key nature of the symbiotic relationship between followers decisions’ to willingly entrust their faith in the leader and the leader’s initiative at leading them. This two-way interaction can endogenously give rise to threshold effects; slight differences in the leader’s ability or the underlying structural conditions can dramatically improve the prospects for successful change. Given the centrality of this leader-follower relationship, we further explore conditions under which an individual may deliberately prefer to follow an ambitious leader with divergent interests rather than a benevolent one with congruent preferences. Thus by virtue of having followers, both ‘good’ and ‘bad’ leaders may be effective at bringing about change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".