Fit and Skill in Employee Selection: Insights from a Study of Headhunters

We draw upon evidence from a qualitative study of headhunters to provide insights into the character and importance of candidate fit and skill for the selection of a broad range of white-collar employees. Headhunters suggest that the fit of a job candidate is assessed at two levels, one corresponding with a general compatibility with organization-level norms, culture, and strategy, the other corresponding more closely with traits and characteristics of the person or persons with whom the job candidate actually interviews. Skill—a factor which is largely neglected by those who tout the importance of fit—also plays an important and independent role in employee selection. Stalls that influence the selection of employees from a pool of candidates tend to be highly specific if not idiosyncratic, and take the form of what headhunters call hot buttons. We conclude by discussing the conceptualization, causes, and implications of fit; we also consider how the importance of fit and hot buttons challenges the explanatory logic of standard accounts of labor-market success.     

静水流深：百年中国蒙学读物研究回顾与展望

中国蒙学读物研究,是诠释传统蒙学思想、实现中华传统文化创造性转化和创新性发展的充分条件,也是探求中国教育学话语体系的重要过程和经验启示。20世纪以来,蒙学读物研究在我国获得长足发展,经历了三个发展阶段：批判基础上内容重构阶段、视角转换基础上价值重审阶段、回溯基础上现代转化阶段。研究主题历经蒙学读物内容适切性评价与审思、价值追问、流布与现代关联研究的变化;研究方法亦有经验归纳法、文献研究法、诠释学方法的主次变换;逐步构建了研究基本框架。研究成果日渐丰厚,但经过系统梳理、总结发现尚存一定程度的“失语”困境、研究方法层次意识和综合抽象深度不足的忧虑,因此,未来需要重点在推进蒙学读物本真诠释和学术话语连接研究、建构综合且有层次蒙学读物研究方法体系、提升研究者的传统文化涵养和学术研究素养三方面用力。     

综合选择指数中的变量选择

本文首次从变量选择的角度把ＡＩＣ准则应用于动物性状的综合选择指数之中,从而获得了最佳的综合选择指数。     

Feature selection based on distance correlation: a filter algorithm

Feature selection (FS) is one of the most powerful techniques to cope with the curse of dimensionality. In the study, a new filter approach to feature selection based on distance correlation is presented (DCFS, for short), which keeps the model-free advantage without any pre-specified parameters. Our method consists of two steps: hard step (forward selection) and soft step (backward selection). In the hard step, two types of associations, between univariate feature and the classes and between group feature and the classes, are involved to pick out the most relevant features with respect to the target classes. Due to the strict screening condition in the first step, some of the useful features are likely removed. Therefore, in the soft step, a feature-relationship gain (like feature score) based on the distance correlation is introduced, which is concerned with five kinds of associations. We sort the feature gain values and implement the backward selection procedure until the errors stop declining. The simulation results show that our method becomes more competitive on several datasets compared with some of the representative feature selection methods based on several classification models.     

A Method to Handle Zero Counts in the Multinomial Model

In the context of an objective Bayesian approach to the multinomial model, Dirichlet(a, …, a) priors with a < 1 have previously been shown to be inadequate in the presence of zero counts, suggesting that the uniform prior (a = 1) is the preferred candidate. In the presence of many zero counts, however, this prior may not be satisfactory either. A model selection approach is proposed, allowing for the possibility of zero parameters corresponding to zero count categories. This approach results in a posterior mixture of Dirichlet distributions and marginal mixtures of beta distributions, which seem to avoid the problems that potentially result from the various proposed Dirichlet priors, in particular in the context of extreme data with zero counts.     

从墓志看宋代士人家庭的择偶行为

墓志的性质在宋代由哀悼文学转向传记文学，更多地记载墓主个人的事迹。本文通过墓志资料考察宋代士人家庭的择偶行为，进一步了解长辈为子女择偶的状况以及当时社会流行的择偶价值观。     

素质教育中的资料信息工作刍议

图书资料工作的质量和水平直接反映一个高等院校的办学质量和水平。在强调“素质教育”的时候,民族高等院校的图书资料工作也同样有一个如何调整自己的思路,开创新的工作局面的问题,从而既要为教学、科研服务,又要为校园文化建设开拓信息源,提供文化支持和信息服务的任务。     

Penalized proportion estimation for non parametric mixture of regressions

Abstract

In this article, we propose a penalized local log-likelihood method to locally select the number of components in non parametric finite mixture of regression models via proportion shrinkage method. Mean functions and variance functions are estimated simultaneously. We show that the number of components can be estimated consistently, and further establish asymptotic normality of functional estimates. We use a modified EM algorithm to estimate the unknown functions. Simulations are conducted to demonstrate the performance of the proposed method. We illustrate our method via an empirical analysis of the housing price index data of United States.     

Nonparametric Estimation of the Number of Drug Users in Hong Kong Using Repeated Multiple Lists

We update a previous approach to the estimation of the size of an open population when there are multiple lists at each time point. Our motivation is 35 years of longitudinal data on the detection of drug users by the Central Registry of Drug Abuse in Hong Kong. We develop a two‐stage smoothing spline approach. This gives a flexible and easily implemented alternative to the previous method which was based on kernel smoothing. The new method retains the property of reducing the variability of the individual estimates at each time point. We evaluate the new method by means of a simulation study that includes an examination of the effects of variable selection. The new method is then applied to data collected by the Central Registry of Drug Abuse. The parameter estimates obtained are compared with the well known Jolly–Seber estimates based on single capture methods.