Unpacking (un)conscious social class bias in faculty hiring processes in Chile: PhD prestige granting university and network

Social class of origin is apparently an imperceptible attribute among doctorate holders seeking academic jobs. Yet, recent studies in different countries reveal that social class of origin may still be influencing the chances of PhD holders from low social class being hired at prestigious universities. Drawing from the theory of social and cultural reproduction, normative ‘fair’ academic hirings frameworks, and qualitative evidence collected in Chile, this research identifies the mechanisms that trigger (un)conscious social class bias in the stages of recruitment and selection of candidates in seven academic departments in economics and industrial engineering (46 interviews). Findings did not prove explicit classism manifestations, but practices of inclusion/exclusion of candidates based on the prestige of PhD-granting universities, and networks. These reproduce the relationship between social class and unequal chances of being connected to prestigious universities. Recommendations to address (un)conscious social class bias in academic hiring are discussed.     

The Multidimensionality of Measurement Bias in High-Stakes Testing: Using Machine Learning to Evaluate Complex Sources of Differential Item Functioning

Test developers and psychometricians have historically examined measurement bias and differential item functioning (DIF) across a single categorical variable (e.g., gender), independently of other variables (e.g., race, age, etc.). This is problematic when more complex forms of measurement bias may adversely affect test responses and, ultimately, bias test scores. Complex forms of measurement bias include conditional effects, interactions, and mediation of background information on test responses. I propose a multidimensional, person-specific perspective of measurement bias to explain how complex sources of bias can manifest in the assessment of human knowledge, skills, and abilities. I also describe a data-driven approach for identifying key sources of bias among many possibilities—namely, a machine learning method commonly known as regularization.     

Open to suggestions: How organizations elicit suggestions through proactive and reactive attention

This paper analyzes organizations’ attempts to entice external contributors to submit suggestions for future organizational action. While earlier work has elaborated on the advantages of leveraging the knowledge of external contributors, our findings show that organizational attempts to attract such involvement are likely to wither and die. We develop arguments about what increases the likelihood of getting suggestions from externals in the future, namely through (1) proactive attention (submitting internally developed suggestions to externals to stimulate debate) and (2) reactive attention (paying attention to suggestions from externals to signal they are being listened to), particularly when those suggestions are submitted by newcomers. Findings from an analysis of about 24,000 initiatives by organizations to involve external contributors suggest these actions are crucial for receiving suggestions from external contributors. Our results are contingent upon the stage of the initiative because organizations’ actions exert more influence in initiatives that lack a history of prior suggestions. Our work has implications for scholars of open innovation because it highlights the importance of considering failures as well successes: focusing exclusively on initiatives that reach a certain stage can lead to partial or erroneous conclusions about why some organizations engage external contributors while others fail.     

基于聚集密度的自适应选择多目标进化算法

分析了线性选择方法的两个缺陷,提出了一种基于聚集密度的非线性自适应选择方法。算法基本思想是：首先将每代种群划分成Pareto劣解集和Pareto非劣解集,然后依照个体的聚集密度分别在劣解集和非劣解集中构造一种偏序集,分别按照不同的等概率在这两个偏序集中选择个体,其中劣解偏序集的个体选择概率远小于非劣解偏序集的个体选择概率,根据两个偏序集中的容量自动计算出两个选择概率。这种非线性选择方法既体现了劣解集和非劣解集中个体的绝对平等性及非劣解集对劣解集的相对优先选择权,又充分考虑到了Pareto最优解的分布性。理论分析和数值计算表明,这种新的选择机制不仅能改善排序选择法的收敛性,而且能得到分布性良好的Pareto最优解。     

竖井多绳摩擦式提升机平衡尾绳选型探讨

根据已知提升机和提升钢丝绳型号,对平衡尾绳进行选型。包括钢丝绳结构选择、抗拉强度选择、等重尾绳的选择。本文还针对在选取不到等重尾绳的情况下,对轻尾绳和重尾绳的选取进行比较和分析。     

技术进步对中国火力发电部门要素偏向与能源强度的影响

在碳达峰、碳中和的背景下,中国电力部门的碳减排一方面依赖于能源结构的优化,另一方面也需要火力发电部门能源强度的降低。本文基于中国火力发电企业层面数据,在超越对数成本函数的框架中纳入企业研发投入、外商直接投资、环境规制3种技术进步来源,采用似不相关回归法估计成本函数系数,进而估算要素替代弹性,分析不同来源技术进步对要素投入及能源强度的影响,讨论技术进步的要素偏向性。研究结论发现：①企业研发投入、外商直接投资的技术溢出效应带来显著的中性技术进步和要素节约,均对能源强度下降有显著贡献;相较于东部企业和大型企业,中西部企业、小型企业的技术进步效应较弱。②环境规制主要通过设备更新等直接效应实现能源偏向性技术进步,而通过企业研发投入的间接机制实现技术进步的影响并不显著。因此,有必要通过促进创新成果推广应用、建设区域间企业交流合作平台、完善环境规制政策组合等措施,激励不同来源技术进步对中国火力发电行业能源要素节约、能源强度下降的作用。     

运动水平和特质焦虑水平对 负性词注意偏向的影响

运用文献资料法、实验法和数理统计法,探讨运动水平、特质焦虑水平对个体负性词注意偏向的影响。依据特质焦虑和运动水平,将受试者分为4组,每组10人,运用2（运动水平）× 2（特质焦虑水平）的多因素组间实验设计方法开展研究。研究结果表明：①高特质焦虑个体存在对负性词的注意偏向,且低运动水平的个体表现更明显;②高特质焦虑个体对负性词存在注意定向偏向和注意固着。以为运动员赛前注意训练提供理论依据。     

对少年跳水运动员走板技术及其训练手段的探讨

通过对跳水运动员初选选材工作的分析，结合具体的选材工作，在跳水初选的具体形态、生理、心理指标方面作相关的探讨。同时，就初选后的一般性身体训练与专项训练及如何结合儿童生理、心理特点，采取行之有效的训练方法作深入探讨。     

面向对象回归测试的方法

对面向对象软件回归测试的方法进行研究,该方法先对类及受影响的类进行分析,找出这些类,然后从测试用例集中找出覆盖所有受影响的类,最后进行测试用例排序进行执行。这种方法有效、经济和安全。     

明代平越卫职役来源地初探

洪武十五年(1382年),平越千户所改置平越卫.自置卫至成化时的八十多年时间中,陆续有武职调入.考察《平越卫选簿》可知,该卫武官主要由南直隶、北直隶和河南调入,旗军主要来自南直隶和湖广地区,部分来自陕西、四川、山西、福建等省区.讨论卫所职役来源地问题,应留意卫所武职与旗军来源地不一致现象.