Glass Walls : Shattering the Six Gender Bias Barriers Still Holding Women Back at Work

“The practicality and clarity make this a valuable contribution to collections in academic and public libraries.” — Booklist, Starred Review

Amy Diehl and Leanne M. Dzubinski wrote a fascinating book on gender bias in the workplace. Glass Walls illuminates the pervasive yet invisible prejudices women face in the workplace. Check it out if you want to understand these hidden biases better! -- Ms. Magazine

A new, important, and richly detailed guide to understanding gender bias with practical solutions for leaders, workplace allies, and individual women.

Gender bias is a powerful but hidden force that is still holding women back, keeping them from achieving their full potential and limiting organizations from achieving the creativity, problem solving, and growth that are possible with a diverse workforce.

In this revealing new book, Amy Diehl and Leanne Dzubinski shine a new light on gender bias in the workplace, uncovering the barriers that work like glass walls surrounding women. Through their original research, they have discovered six core factors and multiple subfactors of bias, giving names to some elements for the first time ever.

Their findings and analysis present a new, important, and richly detailed guidebook to understanding gender bias. They reveal:
- How male privilege, the bedrock on which gender bias is built, results in a workplace created by men and for men.
- How women encounter disproportionate constraints in that workplace, being expected to play supportive roles to men.
- The surprising ways in which women experience insufficient support based on gender.
- The concept of devaluation, and how it tells women they don’t belong at work.
- The troubling ways women face hostility to keep them in their supposed place, merely because of their gender.
- How the combined weight of these barriers leads to acquiescence, when women internalize the obstacles and adapt to the limitations.
- The barriers identified, and the subcomponents of each, are destined to become the framework for understanding gender bias.