EAGER: Leveling the Digital Playing Field for the Job Seeker

EAGER：为求职者打造公平的数字竞争环境

• 批准号: 1537768
• 负责人: Raquel Hill
• 金额: $ 28.98万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537768&HistoricalAwards=false
• 关键词: EAGER; Leveling; Digital; Playing; Field

This project aims to assess how online data impacts the hiring process. In an ideal situation, one might imagine that employers hire the most skilled applicant, but sociological research indicates that this may not be the case. A job applicant's similarity to the interviewer in class background and class-based leisure activities often matters as much or more to employers than a job applicant's skills or work experience. The ability of a recruiter or employer to learn such information from seemingly unrelated data has led researchers to express concerns about privacy, job relevance, and the potential for discrimination. This is the first stage in a larger project that aims to illuminate how online information impacts the ability of job seekers to find employment in post-recession United States. The project creates a framework for identifying systematic patterns of discrimination in regionally specific job markets, and also provide a fuller picture of precisely when in the hiring process are certain forms of discrimination likely to take place (upon submission of resume, at interview stage, and so on). In addition, the collected data will enable job seekers to discover how online information affects their employability, and aid the development of strategies to align online data and professional profiles. The researchers will develop a novel mixed-methods framework to better understand the hiring process and study hiring discrimination by combining ethnographic studies of employers and companies that aggregate applicant profiles; surveys of applicants' background, skillset, and job-seeking history; online profile aggregation; and traditional data mining techniques. This project will contribute to the understanding of how employment works in the United States, and the types of online information that may limit employability. The problem will be addressed across populations that have varying demographic profiles and skill sets, and whose primary industries vary greatly. The proposed analysis will capture regionally specific hiring practices and reveal insights into the kinds of demographic indicators that work for or against job seekers in different regions.

该项目旨在评估在线数据如何影响招聘过程。在理想的情况下，人们可能会想象雇主雇用最熟练的申请人，但社会学研究表明，情况可能并非如此。求职者在阶级背景和基于阶级的休闲活动方面与面试官的相似性对雇主来说往往比求职者的技能或工作经验更重要。招聘人员或雇主从看似不相关的数据中了解此类信息的能力导致研究人员对隐私、工作相关性和潜在歧视表示担忧。这是一个更大项目的第一阶段，该项目旨在阐明在线信息如何影响求职者在经济衰退后的美国找到工作的能力。该项目建立了一个框架，以查明区域特定就业市场中的系统性歧视模式，并更全面地了解在招聘过程中可能发生某些形式歧视的确切时间（提交简历时、面试阶段等）。 此外，收集的数据将使求职者能够发现在线信息如何影响他们的就业能力，并有助于制定战略，使在线数据与专业概况相一致。研究人员将开发一种新的混合方法框架，以更好地了解招聘过程，并通过结合对雇主和公司的人种学研究来研究招聘歧视，这些研究汇总了申请人的个人资料;对申请人背景，技能和求职历史的调查;在线个人资料汇总;和传统的数据挖掘技术。该项目将有助于了解美国的就业情况，以及可能限制就业能力的在线信息类型。这一问题将在人口构成和技能组合各不相同、主要产业差异很大的人群中得到解决。拟议的分析将捕捉特定区域的招聘做法，并揭示对不同区域求职者有利或不利的人口统计指标。