Getting a (big) data-based grip on ideological change. Evidence from Belgian Dutch

2020 ◽  
Vol 8 (1) ◽  
pp. 49-65
Author(s):  
Stefan Grondelaers ◽  
Dirk Speelman ◽  
Chloé Lybaert ◽  
Paul van Gent
Keyword(s):  
Big Data ◽  
Distributional Analysis ◽  
Free Response ◽  
Generational Change ◽  
Value System ◽  
Perceptual Maps ◽  
Experimental Tool ◽  
Virtual Standard ◽  
The One ◽  
Ideological Change

AbstractIn this paper we introduce a computationally enriched experimental tool designed to investigate language ideology (change). In a free response experiment, 211 respondents returned three adjectives in reaction to the labels for five regional varieties, one ethnic variety and two supra-regional varieties of Belgian Dutch, as well as the standard accent of Netherlandic Dutch. Valence information (pertaining to the positive/negative character of the responses) and big data–based distributional analysis (to detect semantic similarity between the responses) were used to cluster the response adjectives into 11 positive and 11 negative evaluative dimensions. Correspondence analysis was subsequently used to compute and visualize the associations between these evaluative dimensions and the investigated language labels, in order to generate “perceptual maps” of the Belgian language repertoire. Contrary to our expectations, these maps unveiled not only the dominant value system which drives standard usage, but also the competing ideology which frames the increasingly occurring non-standard forms. In addition, they revealed a much richer stratification than the “one variety good, all other varieties bad” dichotomy we had anticipated: while VRT-Dutch remains the superior (albeit increasingly virtual) standard for Belgian Dutch, the stigmatized colloquial variety Tussentaal is gradually being accepted as a practical lingua franca, and the Ghent-accent is boosted by modern prestige (dynamism) features. Even more crucially, separate perceptual maps for the older and younger respondents lay bare generational change: there is a growing conceptual proximity between VRT-Dutch and Tussentaal in the younger perceptions.

scholarly journals Challenges of Big Data analysis

2014 ◽  
Vol 1 (2) ◽  
pp. 293-314 ◽  
Author(s):  
Jianqing Fan ◽  
Fang Han ◽  
Han Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Measurement Errors ◽  
Modern Society ◽  
Big Data Analysis ◽  
Small Scale ◽  
Paradigm Change ◽  
Statistical Inferences ◽  
The One ◽  
And Storage

Abstract Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

scholarly journals North Perspectives for a Better South? Big Data and the Global South in Big Data & Society

2019 ◽  
pp. 84-107
Author(s):  
Guilherme Cavalcante Silva
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Global South ◽  
The Other ◽  
The South ◽  
Local Data ◽  
Social Scientists ◽  
Data Practices ◽  
Academic Scholarship ◽  
The One

Over the last few years, data studies within Social Sciences watched a growth in the number of researches highlighting the need for more proficuous participation from the Global South in the debates of the field. The lack of Southern voices in the academic scholarship on the one hand, and of recognition of the importance and autonomy of its local data practices, such as those from indigenous data movements, on the other, had been decisive in establishing a Big Data in the South agenda. This paper displays an analytical mapping of 131 articles published from 2014-2016 in Big Data & Society (BD&S), a leading journal acknowledged for its pioneering promotion of Big Data research among social scientists. Its goal is to provide an overview of the way data practices are approached in BD&S papers concerning its geopolitical instance. It argues that there is a tendency to generalise data practices overlooking the specific consequences of Big Data in Southern contexts because of an almost exclusive presence of Euroamerican perspectives in the journal. This paper argues that this happens as a result of an epistemological asymmetry that pervades Social Sciences.

scholarly journals Sentence first, arguments after: Mechanisms of morphosyntax acquisition

2021 ◽  
Author(s):  
Heidi Getz
Keyword(s):  
Distributional Analysis ◽  
Grammatical Structure ◽  
Natural Languages ◽  
Syntactic Theory ◽  
Closed Class ◽  
Linguistic Representations ◽  
Small Set ◽  
Adults And Children ◽  
The One ◽  
Open Class

Natural languages contain complex grammatical patterns. For example, in German, finite verbs occur second in main clauses while non-finite verbs occur last, as in 'dein Bruder möchte in den Zoo gehen' (“Your brother wants to go to the zoo”). Children easily acquire this type of morphosyntactic contingency (Poeppel & Wexler, 1993; Deprez & Pierce, 1994). There is extensive debate in the literature over the nature of children’s linguistic representations, but there are considerably fewer mechanistic ideas about how knowledge is actually acquired. Regarding German, one approach might be to learn the position of prosodically prominent open-class words (“verbs go 2nd or last”) and then fill in the morphological details. Alternatively, one could work in the opposite direction, learning the position of closed-class morphemes (“-te goes 2nd and -en goes last”) and fitting open-class items into the resulting structure. This second approach is counter-intuitive, but I will argue that it is the one learners take.Previous research suggests that learners focus distributional analysis on closed-class items because of their distinctive perceptual properties (Braine, 1963; Morgan, Meier, & Newport, 1987; Shi, Werker & Morgan, 1999; Valian & Coulson, 1988). The Anchoring Hypothesis (Valian & Coulson, 1988) posits that, because these items tend to occur at grammatically important points in the sentence (e.g., phrase edges), focusing on them helps learners acquire grammatical structure. Here I ask how learners use closed-class items to acquire complex morphosyntactic patterns such as the verb form/position contingency in German. Experiments 1-4 refute concerns that morphosyntactic contingencies like those in German are too complex to learn distributionally. Experiments 5-8 explore the mechanisms underlying learning, showing that adults and children analyze closed-class items as predictive of the presence and position of open-class items, but not the reverse. In these experiments, subtle mathematical distinctions in learners’ input had significant effects on learning, illuminating the biased computations underlying anchored distributional analysis. Taken together, results suggest that learners organize knowledge of language patterns relative to a small set of closed-class items—just as patterns are represented in modern syntactic theory (Rizzi & Cinque, 2016).

Factors of values in the Dutch Language and their relationship to factors of personality

2008 ◽  
Vol 22 (2) ◽  
pp. 81-108 ◽  
Author(s):  
Boele De Raad ◽  
Jan Pieter Van Oudenhoven
Keyword(s):  
Big Five ◽  
Principal Component ◽  
Value Systems ◽  
Regression Analyses ◽  
Value System ◽  
Self And Other ◽  
Guiding Principle ◽  
Five Factors ◽  
Dutch Language ◽  
The One

Following the psycholexical approach, several thousands of potential value descriptors were selected from the Dutch lexicon. This set was subsequently reduced according to criteria of relevance to a list of 641 values. The value list was administered to 634 participants (self‐ and other‐raters), who had to indicate the extent to which each value was a guiding principle in the life of the target. Principal component analyses were performed yielding eight factors of values. In addition, ratings were collected on markers of three other systems of values, including the one described by Schwartz (1992). Finally, A Big Five questionnaire, the FFPI, was administered. Correlation and regression analyses were performed to describe the relations between the different value systems, and between the Dutch value system and the Big Five factors. Copyright © 2007 John Wiley & Sons, Ltd.

The Eclipse of Form in Roland Barthes’s

2020 ◽  
pp. 67-80
Author(s):  
Johnnie Gratton
Keyword(s):  
The Novel ◽  
Value System ◽  
Dominant Term ◽  
René Thom ◽  
The One

This chapter charts the process whereby the text of Barthes’s La Chambre claire sidelines form as a critical concern applicable to photography. An overview of the value system he brings to photography (quite unlike the one he applies to the Novel in the lectures he was delivering contemporaneously) shows that the priority accorded to the referent over the photo as such, to authentication (“ça-a-été”) over representation, and to the disturbing punctum over the disturbed studium, necessarily entails the priority of force over form, not least because each dominant term in these pairs undermines the value of the photograph as something outwardly visual and concretely visible. Force, or intensity, can be tracked not just in the photograph, but also in Barthes’s emotions, whether as beholder of the photo, son in mourning, or essayist repudiating critical sterility, proposing instead to construct a personal phenomenology incorporating the force of affect. A short conclusion via the ideas of René Thom on salient and pregnant forms will suggest a way of bridging the gap between form and force.

Coding Inequality

2020 ◽  
pp. 100-117
Author(s):  
Sarah Brayne
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Social Inequalities ◽  
Big Data Analytics ◽  
The Other ◽  
Surveillance Network ◽  
Data Intensive ◽  
The One ◽  
Reproduction Of Inequality

This chapter looks at the promise and peril of police use of big data analytics for inequality. On the one hand, big data analytics may be a means by which to ameliorate persistent inequalities in policing. Data can be used to “police the police” and replace unparticularized suspicion of racial minorities and human exaggeration of patterns with less biased predictions of risk. On the other hand, data-intensive police surveillance practices are implicated in the reproduction of inequality in at least four ways: by deepening the surveillance of individuals already under suspicion, codifying a secondary surveillance network of individuals with no direct police contact, widening the criminal justice dragnet unequally, and leading people to avoid institutions that collect data and are fundamental to social integration. Crucially, as currently implemented, “data-driven” decision-making techwashes, both obscuring and amplifying social inequalities under a patina of objectivity.

Vehicle to Cloud

2016 ◽  
pp. 182-201
Author(s):  
Alper Ozpinar ◽  
Serhan Yarkan
Keyword(s):  
Big Data ◽  
Environmental Sustainability ◽  
Traffic Management ◽  
Human Beings ◽  
Standard Equipment ◽  
Political Views ◽  
Iot Devices ◽  
The One ◽  
Social Applications ◽  
Use Of Internet

The population of humanity has become more than seven billion. Daily used devices, machines, and equipment, are also increasing quicker than the human population. The number of mobile devices in use like phones, tablets and IoT devices already passed the two billion barrier and even more than one billion as vehicles are also on the roads. Combining these two will make the one of the biggest Big Data Environment about the daily life of human beings after the use of internet and social applications. For the newly manufactured vehicles, internet operated entertainment and information Systems are becoming a standard equipment delivering such an information to the manufacturers but most of the current vehicles do not have a system like that. This chapter explains the combined version of IoT and vehicles to create a V2C vehicle to cloud system that will create the big data for environmental sustainability, energy and traffic management by different technical and political views and aspects.

scholarly journals Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

2020 ◽  
Vol 84 (4) ◽  
pp. 305-314
Author(s):  
Daniel Vietze ◽  
Michael Hein ◽  
Karsten Stahl
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Service Life ◽  
Operating Time ◽  
The Other ◽  
Learning Approaches ◽  
State Of Health ◽  
Remaining Service Life ◽  
Other Hand ◽  
The One

AbstractMost vehicle-gearboxes operating today are designed for a limited service-life. On the one hand, this creates significant potential for decreasing cost and mass as well as reduction of the carbon-footprint. On the other hand, this causes a rising risk of failure with increasing operating time of the machine. Especially if a failure can result in a high economic loss, this fact creates a conflict of goals. On the one hand, the machine should only be maintained or replaced when necessary and, on the other hand, the probability of a failure increases with longer operating times. Therefore, a method is desirable, making it possible to predict the remaining service-life and state of health with as little effort as possible.Centerpiece of gearboxes are the gears. A failure of these components usually causes the whole gearbox to fail. The fatigue life analysis deals with the dimensioning of gears according to the expected loads and the required service-life. Unfortunately, there is very little possibility to validate the technical design during operation, today. Hence, the goal of this paper is to present a method, enabling the prediction of the remaining-service-life and state-of-health of gears during operation. Within this method big-data and machine-learning approaches are used. The method is designed in a way, enabling an easy transfer to other machine elements and kinds of machinery.

scholarly journals Surgical process modeling

2017 ◽  
Vol 2 (3) ◽  
pp. 123-137 ◽  
Author(s):  
Thomas Neumuth
Keyword(s):  
Big Data ◽  
Workflow Management ◽  
Minimal Invasive ◽  
Use Cases ◽  
Process Models ◽  
Surgical Interventions ◽  
Surgical Workflow ◽  
Number Of Patients ◽  
Art Research ◽  
The One

AbstractDue to the rapidly evolving medical, technological, and technical possibilities, surgical procedures are becoming more and more complex. On the one hand, this offers an increasing number of advantages for patients, such as enhanced patient safety, minimal invasive interventions, and less medical malpractices. On the other hand, it also heightens pressure on surgeons and other clinical staff and has brought about a new policy in hospitals, which must rely on a great number of economic, social, psychological, qualitative, practical, and technological resources. As a result, medical disciplines, such as surgery, are slowly merging with technical disciplines. However, this synergy is not yet fully matured. The current information and communication technology in hospitals cannot manage the clinical and operational sequence adequately. The consequences are breaches in the surgical workflow, extensions in procedure times, and media disruptions. Furthermore, the data accrued in operating rooms (ORs) by surgeons and systems are not sufficiently implemented. A flood of information, “big data”, is available from information systems. That might be deployed in the context of Medicine 4.0 to facilitate the surgical treatment. However, it is unused due to infrastructure breaches or communication errors. Surgical process models (SPMs) alleviate these problems. They can be defined as simplified, formal, or semiformal representations of a network of surgery-related activities, reflecting a predefined subset of interest. They can employ different means of generation, languages, and data acquisition strategies. They can represent surgical interventions with high resolution, offering qualifiable and quantifiable information on the course of the intervention on the level of single, minute, surgical work-steps. The basic idea is to gather information concerning the surgical intervention and its activities, such as performance time, surgical instrument used, trajectories, movements, or intervention phases. These data can be gathered by means of workflow recordings. These recordings are abstracted to represent an individual surgical process as a model and are an essential requirement to enable Medicine 4.0 in the OR. Further abstraction can be generated by merging individual process models to form generic SPMs to increase the validity for a larger number of patients. Furthermore, these models can be applied in a wide variety of use-cases. In this regard, the term “modeling” can be used to support either one or more of the following tasks: “to describe”, “to understand”, “to explain”, to optimize”, “to learn”, “to teach”, or “to automate”. Possible use-cases are requirements analyses, evaluating surgical assist systems, generating surgeon-specific training-recommendation, creating workflow management systems for ORs, and comparing different surgical strategies. The presented chapter will give an introduction into this challenging topic, presenting different methods to generate SPMs from the workflow in the OR, as well as various use-cases, and state-of-the-art research in this field. Although many examples in the article are given according to SPMs that were computed based on observations, the same approaches can be easily applied to SPMs that were measured automatically and mined from big data.

scholarly journals Big data: Hell or heaven? Digital platforms and market power in the data-driven economy

2018 ◽  
Vol 23 (3) ◽  
pp. 312-328 ◽  
Author(s):  
Massimiliano Nuccio ◽  
Marco Guerzoni
Keyword(s):  
Big Data ◽  
Economies Of Scale ◽  
A Priori ◽  
Information Economics ◽  
Market Concentration ◽  
Fair Use ◽  
Policy Debate ◽  
Digital Platforms ◽  
Continuous Innovation ◽  
The One

Digital transformation has triggered a process of concentration in several markets for information goods with digital platforms rising to dominate key industries by leveraging on network externalities and economies of scale in the use of consumer data. The policy debate, therefore, focuses on the market control allegedly held by incumbents who build their competitive advantage on big data. In this paper, we evaluate the risk of abuse of a dominant position by analysing three major aspects highlighted in economic theory: entry barriers, price discrimination, and potential for technological improvement. Drawing on industrial and information economics, we argue that the very nature of big data, on the one hand, prompts market concentration and, on the other, limits the possibility of abuse. This claim is not an a-priori apologia of large incumbents in digital markets, but rather an attempt to argue that market concentration is not necessarily detrimental when it stimulates continuous innovation. Nonetheless, the concentration of power in a few global players should raise other concerns linked with the supranational nature of these firms, which can easily cherry-pick locations to exploit tax competition among countries or more favourable privacy legislation and the fair use of data.

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