Detection of words versus good old counting: A note on Mizrahi and Dickinson, “The analytic-continental divide in philosophical practice”

In a recent Metaphilosophy article, Moti Mizrahi and Michael Dickinson argue against characterizing the divide between analytical and continental philosophy as a divide in the use of arguments. This hypothesis is rejected on the basis of a text-mining approach. The present paper argues that the results they extracted do not answer the questions they set out to answer as well as would have been possible. This is due to Mizrahi and Dickinson's choice to disregard duplicate occurrences of argument word pairs, their main indicator for the occurrence of arguments in articles. This paper reconstructs their method by now also counting duplicates. A small corpus (n = 436) of recent (2015–2021) analytical and continental articles is used to rerun the experiment; the results oppose Mizrahi and Dickinson's and suggest that arguments (as operationalized by Mizrahi and Dickinson) occur more in analytical articles. The paper argues that part of the discrepancy derives from the specific methodological choices they made.     

Autonomous vehicle safety: Understanding perceptions of pedestrians and bicyclists

Autonomous vehicle (AV) technologies have been rapidly advancing. One benefit of AVs is that the technology could eliminate many driver errors and also mitigate many pedestrian and bicyclist collisions. Real-world AVs have been tested in many cities. Five companies are running around 50 AVs in Pittsburgh, following the autonomous testing guidelines. BikePGH, a non-profit organization located in Pittsburgh, Pennsylvania conducted a follow-up survey in 2019 (the first survey was conducted in 2017) to understand non-motorists’ opinions of AVs. This study examined how pedestrians and bicyclists perceived AV safety based on their understanding and experiences. At first, this study performed a comparison group test to determine which questions vary by participants’ AV safety rating. The responses were later analyzed with a data mining method known as ‘association rules mining.’ A new performance measure, known as the rule power factor, was then used to identify the significant patterns in the form of rules. The participants also provided their thoughts in responses to the open-ended questions. Using Latent Dirichlet Allocation (LDA), a topic modeling algorithm, 40 topic models were developed based on five open-ended questions. The findings show that the non-motorists showed comparatively fewer negative opinions towards AVs than positive assessments. The results also show that perception patterns vary by the participant’s rating on AV safety. Findings of this study would be beneficial for the AV stakeholders in making AVs and roadways safer for non-motorists.     

Prototypes of the “natural” concepts discovery

In works of Eleanor Rosch “natural” concepts was introduced that reflect a high correlated structure of features of objects of the external world. Prototypes of the “natural” concepts are clearest cases of objects that reflect this highly correlated structure. The same high correlated structure manifested in the “natural” phenotypical classification. To formalize this highly correlated structure, we define a special type of probabilistic causal relations and then probabilistic formal concepts as a cyclically connected probabilistic causal relations. Based on these definitions, we developed a method of prototypes discovery and illustrate it on the example of digits’ prototypes discovery.     

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology     

Authentic leadership,followership, and psychological capital as antecedents of work engagement

The present study investigated authentic leadership, psychological capital, and followership behaviour influences on work engagement of employees. Respondents were 901 South African employees within the healthcare industry organisation (n = 647) and mining industry (n = 254). The employees completed questionnaires on authentic leadership, psychological capital, and followership behaviour, and work engagement. Results following structural equation modelling and mediation analysis suggest work engagement to be explained by the psychological capital of the employee rather than by authentic leadership qualities. Improving employee psychological capital has the potential to enhance the levels of work engagement of employees.     

Assessing the driving distraction effect of vehicle HMI displays using data mining techniques

With the rapid development of human–machine interface (HMI) systems in vehicles, driving distraction caused by HMI displays affects road safety. This study presents a data mining technique to model the four driving distraction indicators: speed deviation, lane departure standard deviation, dwell time, and mean glance time. Driving distraction data was collected on a real-car driving simulator. 3 secondary tasks in 13 mass produced cars were tested by 24 drivers. The random forest algorithm outperformed linear regression, extreme gradient boosting, and multi-layer perceptron as the best model, demonstrating good regression performance as well as good interpretability. The result of random forest showed that the importance of target speed is large for all driving distraction indicators. Among the variables of interaction and user interface design, less step and less on-screen distance of finger movement are efficient for lowering lane departure standard deviation and dwell time. The position of right point is another important variable, and should be between 37 and 47 degrees on a typical sample in this study. A larger angle leads to bigger lane departure, while a smaller angle leads to bigger mean glance time. Most variables of HMI display positioning themselves are not important. This study provides one driving distraction assessment method with a variable impact trend analysis for HMI secondary tasks in an early phase of product development.     

Association rule learning in neuropsychological data analysis for Alzheimer’s disease

Efficient methods of analysis readily available for clinicians continue to be limited within neuropsychological assessment at the raw data level. Here, a novel approach for generating predictive response patterns and analysing neuropsychological raw data is offered. In order to assess the usefulness of association rule learning as an analysis tool for neuropsychological raw data, Frequent Pattern Growth (FP-Growth) was used to mine patterns from the Consortium to Establish a Registry for Alzheimer’s Disease Neuropsychological Battery (CERAD-NB) database. Complete assessment data for 84 post-mortem confirmed Alzheimer’s disease (AD) cases and 294 age, race, and education matched controls were analysed across baseline and one-year follow-up using FP-Growth, for the purpose of assessing the clinical utility of a finer analysis at the raw data level and the feasibility of predicting response patterns for clinical/control groups. Output from FP-Growth, in terms of the number of frequent itemsets retained across both evaluation timepoints, was discernable between controls, mild, and moderate to severe Alzheimer’s disease cases (p < .001, and η² = .488). Patterns within raw data scores, both in terms of frequent itemsets and predictive association rules, appear to be differentiable across groups within neuropsychological analysis, which indicates that FP-Growth is applicable as a supplementary analysis tool for neuropsychological assessment by means of offering an additional level of data analysis, predictive item response capabilities, and aiding in clinical decision making.     

中外两种推理理论的实验比较研究

通过两个实验对中国学者提出的“推理题与推理者的推理知识双重结构模型”和Evans提出的“双重加工理论”进行了实验比较研究。实验一通过两种评定方法对相应性质命题进行评定后所得实验结果表明Evans等(1983)有关“信念效应”研究中的“结论可信性”变量可以视为与胡竹菁等(1996)实验中的“内容正确性”变量是同一性质的变量; 实验二根据“形式正确性”和“内容正确性”两个自变量设计的推理实验结果与Evans等(1983)的研究结果基本一致; 但增加“内容熟悉性”这一自变量设计的推理实验结果表明“推理题与推理者的推理知识双重结构模型”比Evans提出的“双重加工理论”能更好地解释推理者对性质三段论的推理结果。     

Creating cluster‐specific purchase profiles from point‐of‐sale scanner data and geodemographic clusters: improving category management at a major US grocery chain

In the retail grocery industry, category management is the process of managing categories of products for greater profitability and customer value. Category management is a data‐driven process and, as a result, can benefit from point‐of‐sale (POS) scanner data. This paper describes the results of a one‐year project that shows how to use POS scanner data and geodemographic clusters to improve the practice of category management at Price Chopper, a large US grocery chain. The paper demonstrates how to merge POS scanner data with geodemographic clusters to create detailed purchase profiles that provide valuable information to category managers. It also discusses the trials and tribulations of using scanner data and provides several findings as implications (eg store‐specific promotions should be more effective than chain‐wide promotions for stores servicing a small number of geodemographic clusters with distinct shopping profiles) that supermarket managers can immediately use to improve existing promotional strategies. The paper's contents should be relevant to academicians and practitioners interested in improving the practice of category management in the UK, USA, Western Europe and Australia. Copyright © 2004 Henry Stewart Publications.