Battling the Minotaur: How Women Navigate Gender at Work
Bibliographic record
Abstract
Gender parity remains a critical challenge in organizations, with women often navigating a labyrinth of conflicting norms and expectations that hinder their professional advancement. This symposium explores the complexities of gender at work through five innovative papers that examine the influence of gender norms, expectations of backlash, and strategies women employ to achieve their goals. These presentations address diverse topics, including systemic biases in leadership evaluations, a novel scale for measuring gender backlash expectations, a theoretical framework for women’s responses to backlash risks, the transformation of masculine organizational cultures, and the role of negotiation skills in career advancement. Together, these studies challenge traditional narratives of disengagement, emphasizing women’s agency in navigating and reshaping their environments. By integrating theoretical advancements with practical tools and interventions, this symposium offers actionable insights for fostering gender equity and supporting women’s success in organizations. Extraordinary Executive Women: Leader Gender, Atypicality, and Elicited Abstraction Author: Samantha Dodson; University of Calgary Author: Rachael Goodwin; Not Associated Author: Cheryl Wakslak; University of Southern California Author: Jesse Graham; The University of Utah Author: Kristina Diekmann; The University of Utah Assessing the Risks of Breaking Gender Rules: Validation of the Expected Gender Backlash Scale Author: Kileigh Branae Smith; University of Nevada, Reno Author: Kristin Bain; Rochester Institute of Technology Author: Kathryn A. Coll; Author: Alexis Hanna; Navigating the Labyrinth: Women’s Responses to Potential Gender Backlash Author: Amelia Stillwell; The University of Utah Author: Elizabeth R. Tenney; The University of Utah Author: Coco Liu; Author: Kristin Bain; Rochester Institute of Technology Author: Jacqueline Chen; The University of Utah Author: Laura Kray; University of California Berkeley One Small Step for Woman: How Women’s Identity Management Transforms Masculine Defaults Author: Mallory Decker; University of Colorado-Boulder Small Negotiations: Self-Directing the Path of Counterstereotypical Careers Author: Hannah Riley Bowles; Harvard University Author: Deborah Wu; Stonehill College Author: Bobbi Thomason; Pepperdine University Author: Alessandra González; Duke University Author: Ons Ben Abdelkarim; Harvard University Author: Anchal Setia; University of Massachusetts Amherst Author: Rati Thanawala; - Author: Nilanjana Dasgupta; University of Massachusetts Amherst
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".