Introduction

The previous chapters have provided a detailed overview of the variety of ethical challenges posed by the development of ICTs. By way of conclusion, in this epilogue I would like to invite the reader to look into the possible future of Information and Computer Ethics. More specifically, I shall try to forecast how the convergence of two fundamental trends of our times, globalization and the development of the information society, may interact with the ethical problems analysed in this book. The exercise will not be based on some untenable technological determinism. Humanity is, and will remain, firmly in charge of its destiny and hence be responsible for it. Rather, it will mean adopting the farmer's view that, with enough intelligence, toil and a bit of luck, one might be able to tell today what one will probably reap tomorrow. Before trying to ‘look into the seeds of time, and say which grain will grow and which will not’ (Shakespeare, Macbeth, Act I, Scene III, 59–62), two clarifications might be in order.

First, the future of globalization is a phenomenon too complex even to sketch in this brief epilogue. For a synthetic, well-balanced and informed overview, the reader may wish to consult Held and McGrew (2001) and consider that this chapter is written from what Held et al. (1999)) have defined as a ‘transformationalist perspective’, according to which ‘globalization does not simply denote a shift in the extensity or scale of social relations and activity.

Information and Communication Technologies (ICTs) have profoundly altered many aspects of life, including the nature of entertainment, work, communication, education, health care, industrial production and business, social relations and conflicts. As a consequence, they have had a radical and widespread impact on our moral lives and hence on contemporary ethical debates. Consider the following list: PAPA (privacy, accuracy, intellectual property and access); ‘the triple A’ (availability, accessibility and accuracy of information); ownership and piracy; the digital divide; infoglut and research ethics; safety, reliability and trustworthiness of complex systems; viruses, hacking and other forms of digital vandalism; freedom of expression and censorship; pornography; monitoring and surveillance; security and secrecy; propaganda; identity theft; the construction of the self; panmnemonic issues and personal identity; new forms of agency (artificial and hybrid), of responsibility and accountability; roboethics and the moral status of artificial agents; e-conflicts; the re-prioritization of values and virtues…these are only some of the pressing issues that characterize the ethical discourse in our information societies. They are the subject of information and computer ethics (ICE), a new branch of applied ethics that investigates the transformations brought about by ICTs and their implications for the future of human life and society, for the evolution of moral values and rights, and for the evaluation of agents' behaviours.

Since the seventies, ICE has been a standard topic in many curricula. In recent years, there has been a flourishing of new university courses, international conferences, workshops, professional organizations, specialized publications and research centres.

Abstract In this paper we apply various clustering algorithms to the dialect pronunciation data. At the same time we propose several evaluation techniques that should be used in order to deal with the instability of the clustering techniques. The results have shown that three hierarchical clustering algorithms are not suitable for the data we are working with. The rest of the tested algorithms have successfully detected two-way split of the data into the Eastern and Western dialects. At the aggregate level that we used in this research, no further division of sites can be asserted with high confidence.

INTRODUCTION

Dialectometry is a multidisciplinary field that uses various quantitative methods in the analysis of dialect data. Very often those techniques include classification algorithms such as hierarchical clustering algorithms used to detect groups within certain dialect area. Although known for their instability (Jain and Dubes, 1988), clustering algorithms are often applied without evaluation (Goebl, 2007; Nerbonne and Siedle, 2005) or with only partial evaluation (Moisl and Jones, 2005). Very small differences in the input data can produce substantially different grouping of dialects (Nerbonne et al., 2008). Without proper evaluation, it is very hard to determine if the results of the applied clustering technique are an artifact of the algorithm or the detection of real groups in the data.

The aim of this paper is to evaluate algorithms used to detect groups among language dialect varieties measured at the aggregate level. The data used in this research is dialect pronunciation data that consists of various pronunciations of 156 words collected all over Bulgaria.

Abstract In this experimental study, we aim to arrive at a global picture of the mutual intelligibility of various Dutch language varieties by carrying out a computer-controlled lexical decision task in which ten target varieties are evaluated – the Belgian and Netherlandic Dutch standard language as well as four regional varieties of both countries. We auditorily presented real as well as pseudo-words in various varieties of Dutch to Netherlandic and Belgian test subjects, who were asked to decide as quickly as possible whether the items were existing Dutch words or not. The experiment's working assumption is that the faster the subjects react, the better the intelligibility of (the language variety of) the word concerned.

INTRODUCTION

Research framework

When speakers of different languages or language varieties communicate with each other, one group (generally the economically and culturally weaker one) often switches to the language or language variety of the other, or both groups of speakers adopt a third, common lingua franca. However, if the languages or language varieties are so much alike that the degree of mutual comprehension is sufficiently high, both groups of speakers might opt for communicating in their own language variety.

This type of interaction between closely related language varieties, which Haugen (1966) coins semicommunication and Braunmüller and Zeevaert (2001) refer to as receptive multilingualism, has been investigated between speakers of native Indian languages in the United States (Pierce 1952), between Spaniards and Portuguese (Jensen, 1989), between speakers of Scandinavian languages (Zeevaert, 2004; Gooskens, 2006; Lars-Olof Delsing, 2007) and between Slovaks and Czechs (Budovičová, 1987).

Abstract In this paper we relate linguistic, geographic and social distances to each other in order to get a better understanding of the impact the Dutch-German state border has had on the linguistic characteristics of a sub-area of the Kleverlandish dialect area. This area used to be a perfect dialect continuum. We test three models for explaining today's pattern of linguistic variation in the area. In each model another variable is used as the determinant of linguistic variation: geographic distance (continuum model), the state border (gap model) and social distance (social model). For the social model we use perceptual data for friends, relatives and shopping locations. Testing the three models reveals that nowadays the dialect variation in the research area is closely related to the existence of the state border and to the social structure of the area. The geographic spatial configuration hardly plays a role anymore.

INTRODUCTION

The Dutch-German state border south of the river Rhine was established in 1830. Before that time, the administrative borders in this region frequently changed. The Kleverlandish dialect area, which extends from Duisburg in Germany to Nijmegen in The Netherlands, crosses the state border south of the Rhine. The area is demarcated by the Uerdingen line in the south, the diphthongisation line of the West Germanic ‘i’ in the West, and the border with the Low Saxon dialects of the Achterhoek area in the North-East. The geographic details of the area can be found in Figure 1 (the state border is depicted with a dashed-dotted line).

Abstract In this paper the role of concept characteristics in lexical dialectometric research is examined in three consecutive logical steps. First, a regression analysis of data taken from a large lexical database of Limburgish dialects in Belgium and The Netherlands is conducted to illustrate that concept characteristics such as concept salience, concept vagueness and negative affect contribute to the lexical heterogeneity in the dialect data. Next, it is shown that the relationship between concept characteristics and lexical heterogeneity influences the results of conventional lexical dialectometric measurements. Finally, a dialectometric procedure is proposed which downplays this undesired influence, thus making it possible to obtain a clearer picture of the ‘truly’ regional variation. More specifically, a lexical dialectometric method is proposed in which concept characteristics form the basis of a weighting schema that determines to which extent concept specific dissimilarities can contribute to the aggregate dissimilarities between locations.

BACKGROUND AND RESEARCH QUESTIONS

An important assumption underlying most if not all methods of dialectometry is that the automated analysis of the differences in language use between different locations, as they are recorded by dialectologists in large scale surveys, can reveal patterns which directly reflect regional variation. In this paper, in which we focus on lexical variation, we want to address one factor, viz. concept characteristics, which we will claim complicates this picture.

The argumentation which underlies our claim consists of three consecutive logical steps. As a first step, we analyse data taken from a large lexical database of Limburgish dialects in Belgium and The Netherlands, in which we more particularly zoom in on the names for concepts in the field of ‘the human body’.

This is the report of a panel discussion held in connection with the special session on computational methods in dialectology at Methods XIII: Methods in Dialectology on 5 August, 2008 at the University of Leeds. We scheduled this panel discussion in order to reflect on what the introduction of computational methods has meant to our subfield of linguistics, dialectology (in alternative divisions of linguistic subfields also known as variationist linguistics), and whether the dialectologists' experience is typical of such introductions in other humanities studies. Let's emphasise that we approach the question as working scientists and scholars in the humanities rather than as methodology experts or as historians or philosophers of science, i.e. we wished to reflect on how the introduction of computational methods has gone in our own field in order to conduct our own future research more effectively, or alternatively, to suggest to colleagues in neighbouring disciplines which aspects of computational studies have been successful, which have not been, and which might have been introduced more effectively. Since we explicitly wished to reflect not only on how things have gone in dialectology, but also to compare our experiences to others, we invited panellists with broad experience in linguistics and other fields.

We introduce the chair and panellists briefly.

Abstract In this study 91 local Swedish dialects were analysed based on vowel pronunciation. Acoustic measurements of vowel quality were made for 18 vowels of 1,014 speakers by means of principal component analysis of vowel spectra. Two principal components were extracted explaining more than ¾ of the total variance in the vowel spectra. Plotting vowels in the PC1-PC2 plane showed a solution with strong resemblance to vowels in a formant plane. Per location averages of all speakers were calculated and factor analysis was run with the 91 locations as data cases and the two acoustic component of the 18 words as variables. Nine factors were extracted corresponding to distinct geographic distribution patterns. The factor scores of the analysis revealed co-occurrence of a number of linguistic features.

INTRODUCTION

The traditional method of identifying dialect areas has been the so-called isogloss method, where researchers choose some linguistic features that they find representative for the dialect areas and draw lines on maps based on different realisations of these features. One problem with the isogloss method is that isoglosses rarely coincide, and a second is that the choice of linguistic features is subjective and depends on what the researcher chooses to emphasise. Dialectometric research has been trying to avoid these problems by aggregating over large data sets and using more objective data-driven methods when determining dialect areas (Séguy, 1973; Goebl, 1982; Heeringa, 2004; Nerbonne, 2009).