Exploring Policies to Prevent "Passing the Harasser" in Higher Education (2023) / Chapter Skim
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Areas of Further Consideration and Research to Prevent Passing The Harasser
Pages 32-35

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From page 32...
... For example, some institutions may prefer to enact a blanket rejection policy for all candidates with a history of misconduct to ease the burden on their administrators; however, if an institution wants to make hiring decisions at its own discretion based on the information provided, it might use a metric that is primarily about the effectiveness of the informationgathering mechanisms. This may include data related to how often other institutions respond to requests for information, what kinds of institutions are likely to reply, or how closely self-disclosures from applicants align with the information shared by institutions.
From page 33...
... Beyond Hiring This issue paper largely focuses on policies that govern data collection in support of initial hiring decisions, yet the problem of passing the harasser also arises in subsequent appointments, reappointments, promotions, tenure decisions, and transfer between roles within a university or university system. For example, a report from Harvard University explored how a faculty member was able to evade sanctions and take on additional leadership roles and higher degrees of authority as a result of nontransparent policies and incomplete record-keeping (Harvard University, 2021)
From page 34...
... Regarding policies that consider the context around candidates with a history of misconduct and make decisions on a case-by-case basis, the issue is that hiring committees may engage in biased assessments of the candidates based on race or ethnicity. In other words, IHEs may be less likely to hire faculty of color with a history of misconduct because the hiring committee may implicitly perceive race as a factor in the risk the candidate poses.
From page 35...
... Evaluation efforts may also consider this possibility and strive to collect and analyze hiring decisions in a way that elucidates possible bias in implementation.


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