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。     

核准制下投资者行为偏差和中国IPO抑价实证研究

利用行为金融学中投资者行为偏差的观点,对核准制下中国的IPO抑价进行了实证研究。以实行该制度后到股权分置改革前发行并上市的股票为样本,使用Newey—West提出的异方差和自相关一致协方差回归方法,对影响IPO抑价的行为偏差因素进行了实证分析,得出投资者行为偏差是引起中国IPO抑价的结论。     

技术评价偏差及其控制研究

本文讨论技术评价偏差产生的根源、偏差的类型及如何对偏差进行控制的问题。评价偏差的根源主要是来自于评价主体、评价客体及外界因素,偏差类型则根据偏差产生的不同时点分成评价前偏差和评价过程偏差两类。本文提出的评价偏差的控制方法主要针对评价主体。     

Forensic child sexual abuse evaluations: assessing subjectivity and bias in professional judgements

Objectives

Evaluators examining the same evidence often arrive at substantially different conclusions in forensic assessments of child sexual abuse (CSA). This study attempts to identify and quantify subjective factors that contribute to such disagreements so that interventions can be devised to improve the reliability of case decisions.

Methods

Participants included 1106 professionals in the field of child maltreatment representing a range of professional positions or job titles and years of experience. Each completed the Child Forensic Attitude Scale (CFAS), a 28-item survey assessing 3 forensic attitudes believed to influence professional judgments about CSA allegations: emphasis-on-sensitivity (i.e., a focus on minimizing false negatives or errors of undercalling abuse); emphasis-on-specificity (i.e., a focus on minimizing false positives or errors of overcalling abuse); and skepticism toward child and adolescent reports of CSA. A subset of 605 professionals also participated in 1 of 3 diverse decision exercises to assess the influence of the 3 forensic attitudes on ratings of case credibility.

Results

Exploratory factor analysis identified 4 factors or attitude subscales that corresponded closely with the original CFAS scales: 2 subscales for emphasis-on-sensitivity and 1 each for emphasis-on-specificity and skepticism. Attitude subscale scores differed significantly by sample source (in-state trainings vs. national conferences), gender, years of experience, and professional position, with Child Protective Service workers unexpectedly more concerned about overcalling abuse and more skeptical of child disclosures than other professionals—a pattern of scores associated with an increased probability of disbelieving CSA allegations. The 3 decision exercises offered validation of the attitude subscales as predictors of professional ratings of case credibility, with adjusted R²s for the three exercises ranging from .06 to .24, suggesting highly variable effect sizes.

Conclusions

Evaluator disagreements about CSA allegations can be explained, in part, by individual differences in 3 attitudes related to forensic decision-making: emphasis-on-sensitivity, emphasis-on-specificity, and skepticism toward child reports of abuse. These attitudes operate as predispositions or biases toward viewing CSA allegations as likely true or likely false. Several strategies for curbing the influence of subjective factors are highlighted including self-awareness of personal biases and team approaches to assessment.     

Populist Twitter Posts in News Stories

Research in information processing suggests that the journalistic practice of embedding right-wing populist Twitter posts in news may stimulate political polarization. To test this notion, we conducted an online experiment (N?=?379). We investigated how highlighted right-wing populist statements affected individuals from different ideological stances and whether potential effects were determined by Twitter-specific characteristics (Twitter frame, profile picture). We exposed participants to two articles, each including a statement by a politician of the Austrian Freedom Party. In the first group, the statements were not highlighted (control group). In the second and third group, the statements appeared as conventional block quotes either without (G2) or with (G3) a picture of the politician. In the fourth and fifth group, the statements were highlighted as Twitter posts, again either without (G4) or with (G5) a picture of the politician. Results revealed that all highlighting conditions increased statement recognition among left-wing individuals. However, the full Twitter post condition exerted the strongest effect. Higher recognition then decreased left-wing individuals’ anti-immigrant attitudes and sympathy toward the right-wing populist candidate. Thus, embedding right-wing populists’ Twitter posts may induce disconfirmation bias among left-wing voters and trigger a process in which they strengthen their initial attitudes.     

高校创新团队建设研究

本文从创新团队建设的意义入手,通过国内外创新团队建设研究现状,发现创新团队在建设进程中的问题,并针对现有问题提出切实有效的建议。     

Algorithmic bias in data-driven innovation in the age of AI

Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The findings show that there are three major sources of algorithmic bias: data bias, method bias and societal bias. Theoretically, the findings of our study illuminate the role of the dynamic managerial capability to address various biases. Practically, we provide guidelines on addressing algorithmic biases focusing on data, method and managerial capabilities.     

A data quality approach to the identification of discrimination risk in automated decision making systems

Automated decision-making (ADM) systems may affect multiple aspects of our lives. In particular, they can result in systematic discrimination of specific population groups, in violation of the EU Charter of Fundamental Rights. One of the potential causes of discriminative behavior, i.e., unfairness, lies in the quality of the data used to train such ADM systems.Using a data quality measurement approach combined with risk management, both defined in ISO standards, we focus on balance characteristics and we aim to understand how balance indexes (Gini, Simpson, Shannon, Imbalance Ratio) identify discrimination risk in six large datasets containing the classification output of ADM systems.The best result is achieved using the Imbalance Ratio index. Gini and Shannon indexes tend to assume high values and for this reason they have modest results in both aspects: further experimentation with different thresholds is needed.In terms of policies, the risk-based approach is a core element of the EU approach to regulate algorithmic systems: in this context, balance measures can be easily assumed as risk indicators of propagation – or even amplification – of bias in the input data of ADM systems.     

论高校GIS专业教师职业技能

为适应目前我国地理信息系统(GIS)学科的发展以及社会对GIS人才的需求,迫切需要专业教师加强自身职业技能的培养。通过分析目前我国GIS专业高校教师水平参差不齐的情况,进而探讨如何不断丰富专业知识,提高教学质量,成为一名合格的GIS专业教师应具备的职业技能,即扎实的专业知识能力,目标明确的专业教育能力,教育信息资源获取及应用能力,系统化教学设计能力。对于培养高素质的GIS专业教师,进而培养GIS专业创新型人才,具有一定的借鉴和参考意义。     

论民族地区院校特色办学模式改革

民族地区院校建立在民族或地方特色之上的"特色优势学科"的传统办学模式,陷入了"为特色而特色"的办学误区,所培养人才无法适应不断发展的社会对人才成长性的要求。民族地区院校应当建立起以培养学生的自我完善能力为中心的课程体系改革,作为特色办学模式的突破口,通过对学生学习能力的培养,以人才培养的实际社会效果回证民族地区院校特色办学的成功。