Leadership legitimacy and the mobilization of capital(s): Disrupting politics and reproducing heteronormativity
Bibliographic record
Abstract
The rise of populist leaders in the political sphere mounts a challenge to normative understandings of leadership. To better understand this challenge, we examine how political leaders mobilize different forms of social capital in pursuit of leadership legitimacy, providing insight into the dynamics of how leadership norms are maintained. While research has tended to focus on specific forms of capital, this article considers capital as multidimensional and strategically mobilized. The article applies a multimodal analysis to examine interactions between Donald Trump and Hillary Clinton during peak ‘Twitter Moments’ of the three 2016 presidential election debates. We theorize the paradoxical dynamics of the mobilization of multiple capitals and their intersection as a simultaneously disruptive and reproductive resource. While the mobilization of multiple capitals operates to disrupt traditional notions of who can claim legitimacy as a leader in the political field, their disruptive mobilization serves to reproduce implicit heteronormative leadership values. Hence, our theorization illuminates the resilience of implicit leadership values, and their intimate connection with heteronormativity, calling for the need to interrogate leadership legitimacy claims that promise ‘new’ approaches.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".