A CO-LIBERATORY FRAMEWORK FOR BIG DATA

This article provides an account of the ethical issues that arise when digital technologies and online spaces are structured by Big Data algorithms. We show that although the uses of Big Data may be new, traditional theological and ethical categories are still applicable, including “the sins of the fathers” from hamartiology and the scholastic concept of haecceity. Using these resources, we map the overall ecosystem in which digital technologies are developed and used, identifying the relationships between the individuals and organizations involved. We show how these relationships are often characterized by various kinds of harm. We then argue for a reorientation of the tech ecosystem toward co-liberation—and ultimately, joy.     

Caught on camera: Cross-race interactions captured in daily life

The present study examined the prevalence of cross-race interactions occurring in daily life using wearable camera technology that captures photographs of a participants’ immediate environment and social interactions. Coders used appearance-based cues to identify the number of interaction partners captured in the photos who appeared to be White or a racial minority. We found that 29.6% of Whites’ social interactions were with racial minorities and 23.9% of racial minorities’ social interactions were with Whites. Furthermore, we examined whether personality and other individual differences correlated with being captured in more cross-race interactions. We found that Whites who used more emotion-focused coping and less emotional suppression were more likely to be captured engaging in cross-race interactions.     

Sense data and logical relations: Karin Costelloe-Stephen and Russell’s critique of Bergson

ABSTRACT

Though scholarship has explored Karin Costelloe-Stephen’s contributions to the history of psychoanalysis, as well as her relations to the Bloomsbury Group, her philosophical work has been almost completely ignored. This paper will examine her debate with Bertrand Russell over his criticism of Bergson. Costelloe-Stephen had employed the terminology of early analytic philosophy in presenting a number of arguments in defence of Bergson’s views. Costelloe-Stephen would object, among other things, to Russell’s use of an experiment which, as she points out, was first conducted by Carl Stumpf. Russell appeals to Stumpf's experiment in his attempt to prove that sense data are terms in logical relations, a thesis presupposed by the project of logical analysis outlined in Our Knowledge of the External World. A reformulated version of Costelloe-Stephen's argument put forth by this paper shows that Russell's argument fails to provide adequate proof for his thesis. Further modifications of the argument can also address a reconstruction (based on contemporary reports) of Russell's reply to Costelloe-Stephen. In his reply, Russell would use, already in 1914, the term ‘analytic philosophy’ in contrasting his and Moore’s approach to a continental one, exemplified by Bergson and Costelloe-Stephen.     

Estimation of Controlled Direct Effects in Longitudinal Mediation Analyses with Latent Variables in Randomized Studies

Abstract

In a randomized study with longitudinal data on a mediator and outcome, estimating the direct effect of treatment on the outcome at a particular time requires adjusting for confounding of the association between the outcome and all preceding instances of the mediator. When the confounders are themselves affected by treatment, standard regression adjustment is prone to severe bias. In contrast, G-estimation requires less stringent assumptions than path analysis using SEM to unbiasedly estimate the direct effect even in linear settings. In this article, we propose a G-estimation method to estimate the controlled direct effect of treatment on the outcome, by adapting existing G-estimation methods for time-varying treatments without mediators. The proposed method can accommodate continuous and noncontinuous mediators, and requires no models for the confounders. Unbiased estimation only requires correctly specifying a mean model for either the mediator or the outcome. The method is further extended to settings where the mediator or outcome, or both, are latent, and generalizes existing methods for single measurement occasions of the mediator and outcome to longitudinal data on the mediator and outcome. The methods are utilized to assess the effects of an intervention on physical activity that is possibly mediated by motivation to exercise in a randomized study.     

Log Data Analysis with ANFIS: A Fuzzy Neural Network Approach

This study explores the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS), a neuro-fuzzy approach, to analyze the log data of technology-based assessments to extract relevant features of student problem-solving processes, and develop and refine a set of fuzzy logic rules that could be used to interpret student performance. The log data that record student response processes while solving a science simulation task were analyzed with ANFIS. Results indicate the ANFIS analysis could generate and refine a set of fuzzy rules that shed lights on the process of how students solve the simulation task. We conclude the article by discussing the advantages of combining human judgments with the learning capacity of ANFIS for log data analysis and outlining the limitations of the current study and areas of future research.     

Unobtrusive indicators of culture for organizations: a systematic review

ABSTRACT

We systematically reviewed the literature using unobtrusive measures to study organizational culture. To synthesize, theorize, and evaluate this research, we introduce the concept of an unobtrusive indicator of culture (UIC) for organizations. A UIC measures organizational culture through collecting data without engaging employees, and is conceptualized in terms of cultural artefacts. We identified thirty-five articles, containing 135 distinct UICs, drawn from 16 distinct data sources. UICs coalesced into two groups. First, textual UICs, with culture measured through language patterns in annual reports, employee online reviews, and emails. Second, UICs focusing on organizational practices, for instance, organizational policies or executive rewards. Over two-thirds (68%) of UICs measured values for integrity, results orientation, and clan cultures, and we conjecture that UICs may be most useful for studying aspects of culture sensitive to reporting biases, and benchmarking large samples of organizations. Forty-eight percent of UICs had good or promising construct validity: many were textual UICs, and those focusing on organizational practices were less established. UICs can potentially advance the study of organizational culture, yet must be developed and applied cautiously, with careful consideration of their advantages and limitations, and how they complement existing measurements and conceptualizations of culture.     

Neutrosophic data formation using Gaussian filter based costas coding for wireless communication systems

Outstanding advantages of OFDM helps high data rate communication systems such as Digital Video Broadcasting (DVB) and mobile worldwide interoperability for microwave access (mobile Wi-MAX). But, OFDM system grieves from grave issue of high PAPR. In OFDM system data output is superposition of multiple sub-carriers and leads to big data. In this case some instantaneous power output might increase greatly and become far higher than the mean power of system. In order to make Neutrosophic data for less PAPR criteria, Gaussian filter based Costas coding is proposed. This paper also proposes feeding the Orthogonal Frequency Division Multiplexed signal (OFDM) into a phase accumulator to obtain frequency modulation, with the aim of reducing the peak to average power ratio (PAPR). For associated radar with modulated waveform mentioned here can be used to enhance the performance. It is widely used to analyze the variation of the output waveform further, the implementation results using MATLAB show that the ratio of average power to peak power is reducing considerably in comparing with the conventional method. Consequently; the signal can assume to fade in a frequency selective channel without an equalizer.     

A new goodness-of-fit measure for probit models: Surrogate R2

Probit models are used extensively for inferential purposes in the social sciences as discrete data are prevalent in a vast body of social studies. Among many accompanying model inference problems, a critical question remains unsettled: how to develop a goodness-of-fit measure that resembles the ordinary least square (OLS) R² used for linear models. Such a measure has long been sought to achieve ‘comparability’ of different empirical models across multiple samples addressing similar social questions. To this end, we propose a novel R² measure for probit models using the notion of surrogacy – simulating a continuous variable $S$ as a surrogate of the original discrete response (Liu & Zhang, Journal of the American Statistical Association, 113, 845 and 2018). The proposed R² is the proportion of the variance of the surrogate response explained by explanatory variables through a linear model, and we call it a surrogate R². This paper shows both theoretically and numerically that the surrogate R² approximates the OLS R² based on the latent continuous variable, preserves the interpretation of explained variation, and maintains monotonicity between nested models. As no other pseudo R², McKelvey and Zavoina's and McFadden's included, can meet all the three criteria simultaneously, our measure fills this crucial void in probit model inference.