选择(遗传算法)
忠诚
适应性
构造(python库)
样品(材料)
计算机科学
机器学习
心理学
人工智能
生态学
色谱法
电信
生物
化学
程序设计语言
作者
Elaine D. Pulakos,Rose A. Mueller-Hanson,J. Stuart Nelson
出处
期刊:Oxford University Press eBooks
[Oxford University Press]
日期:2012-11-21
卷期号:: 595-613
被引量:3
标识
DOI:10.1093/oxfordhb/9780199732579.013.0026
摘要
Abstract In this chapter, we examine issues relevant to incorporating trainability and adaptive performance into selection research. We adopt the definition of adaptive performance suggested by Pulakos et al. (2000) that specified eight dimensions defining this construct. One of these dimensions, leaning new tasks, technology, and procedures, was used to define trainability. We then examine recent models of adaptive performance and training to identify likely predictors of adaptability and trainability and propose a method for determining when and where these criteria should be included and explicitly predicted in selection research. We examine the pros and cons associated with different criterion measures and recommend that typical rating measures potentially supplemented by lower-fidelity work sample measures be incorporated in selection research. Finally, we discuss gaps in the current literature and recommend areas for future research.
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