arXiv:2512.05350cs.SEcs.AI2025-12

揭示软件工程中神经多样性女性的独特困境与应对策略

Invisible Load: Uncovering the Challenges of Neurodivergent Women in Software Engineering

  • 融合包容框架与性别认知方法,构建针对神经多样性女性的调研流程
  • 识别出认知、社交、组织等五类挑战,其中误诊与掩饰加剧排斥
  • 适合关注职场包容性、性别与神经多样性交叉议题的研究者参考

软件工程中的神经多样性女性面临性别偏见与神经差异交织的独特挑战。尽管职场神经多样性日益受到关注,但此前尚无研究系统探讨这一群体。误诊、掩饰行为及以男性为中心的工作文化持续加剧压力、倦怠与人才流失。为此,我们提出一种混合方法,结合InclusiveMag包容性框架与GenderMag可用性评估流程,分三阶段展开:文献综述、人物画像与分析流程构建、协同工作坊应用。通过针对性文献回顾,将挑战归纳为认知、社交、组织、结构及职业发展五大类,揭示误诊或迟诊、自我掩饰如何加剧边缘化。这些发现为后续开发可行动的包容性分析方法奠定基础。

原文摘要 · Abstract (English)

Neurodivergent women in Software Engineering (SE) encounter distinctive challenges at the intersection of gender bias and neurological differences. To the best of our knowledge, no prior work in SE research has systematically examined this group, despite increasing recognition of neurodiversity in the workplace. Underdiagnosis, masking, and male-centric workplace cultures continue to exacerbate barriers that contribute to stress, burnout, and attrition. In response, we propose a hybrid methodological approach that integrates InclusiveMag's inclusivity framework with the GenderMag walkthrough process, tailored to the context of neurodivergent women in SE. The overarching design unfolds across three stages, scoping through literature review, deriving personas and analytic processes, and applying the method in collaborative workshops. We present a targeted literature review that synthesize challenges into cognitive, social, organizational, structural and career progression challenges neurodivergent women face in SE, including how under/late diagnosis and masking intensify exclusion. These findings lay the groundwork for subsequent stages that will develop and apply inclusive analytic methods to support actionable change.

神经多样性女性工程师职场包容

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