Growing up in Affiliation with a Religious Community : A Case Study of Seventh-day Adventist Youth in Finland

This study investigates how the religious community as a socialization context affects the development of young people's religious identity and values, using Finnish Seventh-day Adventism as a context for the case study. The research problem is investigated through the following questions: (1) What aspects support the intergenerational transmission of values and tradition in religious home education? (2) What is the role of social capital and the social networks of the religious community in the religious socialization process? (3) How does the religious composition of the peer group at school (e.g., a denominational school in comparison to a mainstream school) affect these young people s social relations and choices and their religious identity (as challenged versus as reinforced by values at school)? And (4) How do the young people studied negotiate their religious values and religious membership in the diverse social contexts of the society at large? The mixed method study includes both quantitative and qualitative data sets (3 surveys: n=106 young adults, n=100 teenagers, n=55 parents; 2 sets of interviews: n=10 young adults and n=10 teenagers; and fieldwork data from youth summer camps). The results indicate that, in religious home education, the relationship between parents and children, the parental example of a personally meaningful way of life, and encouraging critical thinking in order for young people to make personalized value choices were important factors in socialization. Overall, positive experiences of the religion and the religious community were crucial in providing direction for later choices of values and affiliations. Education that was experienced as either too severe or too permissive was not regarded as a positive influence for accepting similar values and lifestyle choices to those of the parents. Furthermore, the religious community had an important influence on these young people s religious socialization in terms of the commitment to denominational values and lifestyle and in providing them with religious identity and rooting them in the social network of the denomination. The network of the religious community generated important social resources, or social capital, for both the youth and their families, involving both tangible and intangible benefits, and bridging and bonding effects. However, the study also illustrates the sometimes difficult negotiations the youth face in navigating between differentiation and belonging when there is a tension between the values of a minority group and the larger society, and one wants to and does belong to both. It also demonstrates the variety within both the majority and the minority communities in society, as well as the many different ways one can find a personally meaningful way of being an Adventist. In the light of the previous literature about socialization-in-context in an increasingly pluralistic society, the findings were examined at four levels: individual, family, community and societal. These were seen as both a nested structure and as constructing a funnel in which each broader level directs the influences that reach the narrower ones. The societal setting directs the position and operation of religious communities, families and individuals, and the influences that reach the developing children and young people are in many ways directed by societal, communal and family characteristics. These levels are by nature constantly changing, as well as being constructed of different parts, like the pieces of a jigsaw puzzle, each of which alters in significance: for some negotiations on values and memberships the parental influence may be greater, whereas for others the peer group influences are. Although agency does remain somewhat connected to others, the growing youth are gradually able to take more responsibility for their own choices and their agency plays a crucial role in the process of choosing values and group memberships. Keywords: youth, community, Adventism, socialization, values, identity negotiations

On the Problem of Participation in Strategy: A Critical Discursive Perspective

We still know little of why strategy processes often involve participation problems. In this paper, we argue that this crucial issue is linked to fundamental assumptions about the nature of strategy work. Hence, we need to examine how strategy processes are typically made sense of and what roles are assigned to specific organizational members. For this purpose, we adopt a critical discursive perspective that allows us to discover how specific conceptions of strategy work are reproduced and legitimized in organizational strategizing. Our empirical analysis is based on an extensive research project on strategy work in 12 organizations. As a result of our analysis, we identify three central discourses that seem to be systematically associated with nonparticipatory approaches to strategy work: “mystification,” “disciplining,” and “technologization.” However, we also distinguish three strategy discourses that promote participation: “self-actualization,” “dialogization,” and “concretization.” Our analysis shows that strategy as practice involves alternative and even competing discourses that have fundamentally different kinds of implications for participation in strategy work. We argue from a critical perspective that it is important to be aware of the inherent problems associated with dominant discourses as well as to actively advance the use of alternative ones.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Topics in Spatial Econometrics - With Applications to House Prices

Topics in Spatial Econometrics — With Applications to House Prices Spatial effects in data occur when geographical closeness of observations influences the relation between the observations. When two points on a map are close to each other, the observed values on a variable at those points tend to be similar. The further away the two points are from each other, the less similar the observed values tend to be. Recent technical developments, geographical information systems (GIS) and global positioning systems (GPS) have brought about a renewed interest in spatial matters. For instance, it is possible to observe the exact location of an observation and combine it with other characteristics. Spatial econometrics integrates spatial aspects into econometric models and analysis. The thesis concentrates mainly on methodological issues, but the findings are illustrated by empirical studies on house price data. The thesis consists of an introductory chapter and four essays. The introductory chapter presents an overview of topics and problems in spatial econometrics. It discusses spatial effects, spatial weights matrices, especially k-nearest neighbours weights matrices, and various spatial econometric models, as well as estimation methods and inference. Further, the problem of omitted variables, a few computational and empirical aspects, the bootstrap procedure and the spatial J-test are presented. In addition, a discussion on hedonic house price models is included. In the first essay a comparison is made between spatial econometrics and time series analysis. By restricting the attention to unilateral spatial autoregressive processes, it is shown that a unilateral spatial autoregression, which enjoys similar properties as an autoregression with time series, can be defined. By an empirical study on house price data the second essay shows that it is possible to form coordinate-based, spatially autoregressive variables, which are at least to some extent able to replace the spatial structure in a spatial econometric model. In the third essay a strategy for specifying a k-nearest neighbours weights matrix by applying the spatial J-test is suggested, studied and demonstrated. In the final fourth essay the properties of the asymptotic spatial J-test are further examined. A simulation study shows that the spatial J-test can be used for distinguishing between general spatial models with different k-nearest neighbours weights matrices. A bootstrap spatial J-test is suggested to correct the size of the asymptotic test in small samples.

The Impact of Readily Detected- and Underlying Attributes on Social Integration in Cross-Cultural Settings

The benefits and drawbacks of homogeneity and heterogeneity have been debated at length. Whereas some researchers assert that heterogeneity is beneficial for groups that are engaged in complex problem solving, the other researchers emphasize the potential costs associated with diversity. The inconsistency is a result of the incomplete measurement of diversity and focus one or two types of diversity. Most research concentrates on the readily detected/visible characteristics, making the assumption that such characteristics are related to underlying attributes (e.g., attitudes and values). In many cases, the demographic characteristics do not covary perfectly with the psychological attributes. Thus both types of attributes need to be utilized to fully understand the impact of diversity. The present research with four essays takes into account both types of attributes and tests their impact on social integration in cross-cultural settings. The results indicate that: (1) readily detectable- and underlying attributes are not related; (2) diversity has overall a negative impact on social integration; (3) socio-cultural context potentially influences the salience of diversity; and (4) diversity and social integration influences the formation of social cognition in form of transactive memory directories. The limits of research and managerial implications are discussed.

Four-dimensional current algebra from Chern-Simons theory

We study an abelian Chern-Simons theory on a five-dimensional manifold with boundary. We find it to be equivalent to a higher-derivative generalization of the abelian Wess-Zumino-Witten model on the boundary. It contains a U(1) current algebra with an operational extension.

An approximate algorithm for the minimal vertex nested polygon problem

Given two simple polygons, the Minimal Vertex Nested Polygon Problem is one of finding a polygon nested between the given polygons having the minimum number of vertices. In this paper, we suggest efficient approximate algorithms for interesting special cases of the above using the shortest-path finding graph algorithms.

Hardness of approximation results for the problem of finding the stopping distance in Tanner graphs

Tanner Graph representation of linear block codes is widely used by iterative decoding algorithms for recovering data transmitted across a noisy communication channel from errors and erasures introduced by the channel. The stopping distance of a Tanner graph T for a binary linear block code C determines the number of erasures correctable using iterative decoding on the Tanner graph T when data is transmitted across a binary erasure channel using the code C. We show that the problem of finding the stopping distance of a Tanner graph is hard to approximate within any positive constant approximation ratio in polynomial time unless P = NP. It is also shown as a consequence that there can be no approximation algorithm for the problem achieving an approximation ratio of 2(log n)(1-epsilon) for any epsilon > 0 unless NP subset of DTIME(n(poly(log n))).

SPSA algorithms with measurement reuse

Four algorithms, all variants of Simultaneous Perturbation Stochastic Approximation (SPSA), are proposed. The original one-measurement SPSA uses an estimate of the gradient of objective function L containing an additional bias term not seen in two-measurement SPSA. As a result, the asymptotic covariance matrix of the iterate convergence process has a bias term. We propose a one-measurement algorithm that eliminates this bias, and has asymptotic convergence properties making for easier comparison with the two-measurement SPSA. The algorithm, under certain conditions, outperforms both forms of SPSA with the only overhead being the storage of a single measurement. We also propose a similar algorithm that uses perturbations obtained from normalized Hadamard matrices. The convergence w.p. 1 of both algorithms is established. We extend measurement reuse to design two second-order SPSA algorithms and sketch the convergence analysis. Finally, we present simulation results on an illustrative minimization problem.

Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources

This paper presents a glowworm metaphor based distributed algorithm that enables a collection of minimalist mobile robots to split into subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple radiation sources such as nuclear/hazardous chemical spills and fire-origins in a fire calamity. The algorithm is based on a glowworm swarm optimization (GSO) technique that finds multiple optima of multimodal functions. The algorithm is in the same spirit as the ant-colony optimization (ACO) algorithms, but with several significant differences. The agents in the glowworm algorithm carry a luminescence quantity called luciferin along with them. Agents are thought of as glowworms that emit a light whose intensity is proportional to the associated luciferin. The key feature that is responsible for the working of the algorithm is the use of an adaptive local-decision domain, which we use effectively to detect the multiple source locations of interest. The glowworms have a finite sensor range which defines a hard limit on the local-decision domain used to compute their movements. Extensive simulations validate the feasibility of applying the glowworm algorithm to the problem of multiple source localization. We build four wheeled robots called glowworms to conduct our experiments. We use a preliminary experiment to demonstrate the basic behavioral primitives that enable each glowworm to exhibit taxis behavior towards source locations and later demonstrate a sound localization task using a set of four glowworms.

Component Inventory Costs in an Assembly Problem with Uncertain Supplier Lead-Times

We present a generic study of inventory costs in a factory stockroom that supplies component parts to an assembly line. Specifically, we are concerned with the increase in component inventories due to uncertainty in supplier lead-times, and the fact that several different components must be present before assembly can begin. It is assumed that the suppliers of the various components are independent, that the suppliers' operations are in statistical equilibrium, and that the same amount of each type of component is demanded by the assembly line each time a new assembly cycle is scheduled to begin. We use, as a measure of inventory cost, the expected time for which an order of components must be held in the stockroom from the time it is delivered until the time it is consumed by the assembly line. Our work reveals the effects of supplier lead-time variability, the number of different types of components, and their desired service levels, on the inventory cost. In addition, under the assumptions that inventory holding costs and the cost of delaying assembly are linear in time, we study optimal ordering policies and present an interesting characterization that is independent of the supplier lead-time distributions.