How Women in Leadership are Influenced by and Influence Their Social Environments
Bibliographic record
Abstract
Women are exposed to and shaped by societal expectations and biases. They face societal stereotypes and biases that shape the experience of leadership in ways that constrain their agency and advancement. However, some of them have navigated the challenges and gained a foothold by bringing in new perspectives and leadership styles that positively transform organizational and societal cultures. Such seemingly equivocal findings of women’s experiences in strategic leadership positions suggest a potential opportunity for theorizing and exploring the contextual conditions that determine the ways that women continue to shape and are shaped by the social environment around them. This symposium bridges the macro and micro divide to highlight not only how entities in the social environments, such as regulatory bodies, media, and online forums, continue to disadvantage women leaders but also how women in strategic leadership positions build on their leadership styles, social ties, and cognitive and behavioral factors to influence the social environment. As such, it examines such characteristics as both a cause and a consequence of women in leadership positions, to help uncover boundary conditions to existing theories related to gender diversity and social environments and bridge existing theories in the micro- and macro-organizational domains. Overall, the studies included in this symposium showcase how social environments influence the meaning of—and are influenced by—gender and diversity in leadership positions. Walking on Broken Glass: Status Cues and Early-Stage CEO Evaluations Author: Christine Shropshire; Arizona State U. Author: Abbie Griffith Oliver; U. of Virginia Rumor Has It: CEO Gender and Responses to Organizational Denials Author: Nicole Montgomery; U. of Virginia - McIntire School of Commerce Author: Amanda Cowen; U. of Virginia Examining The Glass Ceiling in The U.S. Labor Movement Author: Rachel Aleks; U. of Windsor Author: Tina Saksida; U. of Prince Edward Island Author: Aaron Wolf; SETI Institute Management Gender Diversity and Labor Law Violations Author: Tianhua Cao; Indiana U. Kokomo Author: Bidisha Chakrabarty; Saint Louis U. Author: Vishal K. Gupta; U. of Alabama Author: Sandra Mortal; U. of Alabama Can I Get an Upgrade? How Female CEOs Leverage Their Scarcity to Gain Prestigious Board Appointments Author: Yashodhara Basuthakur; Texas A&M U., Mays Business School Author: Danuse Bement; U. of Notre Dame Author: Priyanka Dwivedi; Texas A&M U., Mays Business School Author: Michael Deane Howard; Iowa State U.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".