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This article investigates the prevalence dynamics of such terms in academic papers published in all fields of knowledge. The literature review uses the Semantic Scholar Open Research Corpus (SSORC) containing, as of 2020, over 175 million published scholarly articles and associated metadata. The author quantifies the prevalence of words denoting prejudice against ethnicity, gender, sexual orientation, gender identity, minority religious sentiment, age, body weight and disability in SSORC abstracts over the period 1970-2020. He then examines the relationship between the prevalence of such terms in the academic literature and their concomitant prevalence in news media content. Finally, he also analyzes the temporal dynamics of an additional set of terms associated with social justice discourse in both the scholarly literature and in news media content. Taken together, these analyses allows to illuminate the chronological dynamics of prejudice and social justice themes in academic discourse as well as their relationship with news media content.

Author(s): David Rozado [sup.1] Author Affiliations: (1) grid.462693.c, 0000 0001 0695 6386, Otago Polytechnic, , Forth Street, 9054, Dunedin, New Zealand Introduction This work examines word frequency usage in New [...]

The term diversity can be operationalized demographically (in terms of physical or external characteristics such as race, gender, ethnicity and nationality) or intellectually (in terms of mental phenomena such as viewpoints, beliefs, ideas and political opinion). This work examines the context in which the concept of diversity is used by 50 US elite universities in their online institutional presence. Distributional semantics theory is leveraged to quantify semantic similarity between linguistic items based on their distributional properties in a large sample of language data taken from universities online profiles. The language modelling is carried out using Word2vec, a state-of-the-art machine learning model widely used by the natural language processing community to create vector representations of words (i.e. word embeddings). The model uses a neural network trained to reconstruct the linguistic context of words in the training corpus. As a by-product of the training objective, word2vec embeds words into a learned vector space where words that share common contexts and thus semantic meaning according to the distributional hypotheses, are located in close proximity to one another. A quantitative analysis of cosine similarities between word vectors derived from the corpus of text retrieved from universities online institutional profiles shows that the diversity concept is much closer to demographic operationalisations of diversity such as race, gender, ethnicity or nationality than to intellectual ones such as viewpoints, values, beliefs or political orientation. That is, the universities studied tend to use the word diversity predominantly in its demographic denotation to refer to variety of external appearance instead of to variety of mental phenomena. This is significant in light of the severe lack of ideological diversity in universities across the US, with the vast majority of faculty leaning left of center. Universities emphasis on the usage of the term diversity to denote demographic subtypes of diversity could be indicative of a majority power structure in the Academy which tries to hinder the fostering of viewpoint diversity by steering diversity efforts towards demographic interpretations of the word. At the very least, the results of this work suggest that universities, as judged from the way they use language in their own online institutional profiles, prioritize demographic types of diversity around variety of external appearance cues over intellectual heterogeneity., Author(s): David Rozado [sup.1] Author Affiliations: (Aff1) 0000 0001 0695 6386, grid.462693.c, School of Information Technology, Otago Polytechnic, , Dunedin, New Zealand The term diversity within human groups refers to [...]

Author(s): David Rozado [sup.1] , Stephen Atkins [sup.1] Author Affiliations: (Aff1) 0000 0001 0695 6386, grid.462693.c, Otago Polytechnic, , Dunedin, New Zealand Numerous studies have shown the severe lack of [...]

Most citizens in modern liberal democracies regularly consume news media content to inform themselves about current affairs. Thus, content analysis of news and opinion articles from popular media outlets can provide rich insight about the cultural milieu where such textual artifacts originated. Combining computational tools for content analysis with human led finesse can help an analyst exploit the capabilities of scalable computational methods while also leveraging human skills and expertise to guide the analysis. This work introduces an online tool, the http://media-analytics.org website, that empowers researchers by providing modern analytics tools to study language usage in textual content from news and opinion articles of major media outlets. Due to the diachronic nature of news articles, the Media-Analytics.org website allows the exploration of temporal dynamics in word frequency usage and strength of association between word pairs. It is the hope of the author that these tools can help other researchers gain insights about the temporal flux in language usage by major news media organizations.

This work analyzes the prevalence of words denoting prejudice in 27 million news and opinion articles written between 1970 and 2019 and published in 47 of the most popular news media outlets in the United States. Our results show that the frequency of words that denote specific prejudice types related to ethnicity, gender, sexual, and religious orientation has markedly increased within the 2010–2019 decade across most news media outlets. This phenomenon starts prior to, but appears to accelerate after, 2015. The frequency of prejudice-denoting words in news articles is not synchronous across all outlets, with the yearly prevalence of such words in some influential news media outlets being predictive of those words' usage frequency in other outlets the following year. Increasing prevalence of prejudice-denoting words in news media discourse is often substantially correlated with U.S. public opinion survey data on growing perceptions of minorities' mistreatment. Granger tests suggest that the prevalence of prejudice-denoting terms in news outlets might be predictive of shifts in public perceptions of prejudice severity in society for some, but not all, types of prejudice. [ABSTRACT FROM AUTHOR]

Human-computer interaction systems that bypass manual control can be beneficial for many use cases, including users with severe motor disability. We investigated pupillometry (inferring mental activity via dilations of the pupil) as an interaction method because it is noninvasive, easy to analyse, and increasingly available for practical development. In 3 experiments we investigated the efficacy of using pupillometry to detect imaginary motor movements of the hand. In Experiment 1 we demonstrated that, on average, the pupillary response is greater when the participant is imagining a hand-grasping motion, as compared with the control condition. In Experiment 2 we investigated how imaginary hand-grasping affects the pupillary response over time. In Experiment 3 we employed a simple classifier to demonstrate single-trial detection of imagined motor events using pupillometry. Using the mean pupil diameter of a single trial, accuracy rates as high as 71.25%, were achieved. Implications for the development of a pupillometry-based switch and future directions are discussed. [ABSTRACT FROM AUTHOR]