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The object of this research is the reconstruction of the existing legal response by European Union states to the phenomenon of immigration. It seeks to analyse the process of conferral of protection.

One main dimension is selected and discussed: the case law of the national courts. The study focuses on the legal status of immigrants resulting from the intervention of these national courts.

The research shows that although the courts have conferred an increasing protection on immigrants, this has not challenged the fundamental principle of the sovereignty of the states to decide, according to their discretionary prerogatives, which immigrants are allowed to enter and stay in their territories. Notwithstanding the differences in the general constitutional and legal structures, the research also shows that the courts of the three countries considered – France, Germany and Spain – have progressively moved towards converging solutions in protecting immigrants.

The research contributes to a better understanding of the different legal orders analysed.

The purpose of this paper is to establish a version of a theorem that originated from population genetics and has been later adopted in evolutionary computation theory that will lead to novel Monte‐Carlo sampling algorithms that provably increase the AI potential.

In the current paper the authors set up a mathematical framework, state and prove a version of a Geiringer‐like theorem that is very well‐suited for the development of Mote‐Carlo sampling algorithms to cope with randomness and incomplete information to make decisions.

This work establishes an important theoretical link between classical population genetics, evolutionary computation theory and model free reinforcement learning methodology. Not only may the theory explain the success of the currently existing Monte‐Carlo tree sampling methodology, but it also leads to the development of novel Monte‐Carlo sampling techniques guided by rigorous mathematical foundation.

The theoretical foundations established in the current work provide guidance for the design of powerful Monte‐Carlo sampling algorithms in model free reinforcement learning, to tackle numerous problems in computational intelligence.

Establishing a Geiringer‐like theorem with non‐homologous recombination was a long‐standing open problem in evolutionary computation theory. Apart from overcoming this challenge, in a mathematically elegant fashion and establishing a rather general and powerful version of the theorem, this work leads directly to the development of novel provably powerful algorithms for decision making in the environment involving randomness, hidden or incomplete information.

The paper aims at providing a conceptual framework based on clearly defined concepts and notions, which integrates goals into process modeling and specifically distinguishes goals from soft‐goals or business measures. The application of this framework facilitates a systematic use of soft‐goals in process design.

The framework is developed on the basis of Bunge's well‐established ontology. It is applied to processes taken from the SCOR supply chain reference model for demonstration and evaluation.

Applying the framework to the SCOR processes resulted in a set of focused relations between soft‐goals and processes, as opposed to the ones suggested originally in the SCOR model. This demonstrates the usefulness of the framework in process design.

The approach presented in the paper is still rather a theoretical framework than a fully validated procedure. It should be tested on larger‐scale cases in more practical settings and evaluated accordingly.

Applying the clearly defined concepts of the framework and the suggested analysis procedure is expected to lead to focused and applicable measures tied to business process during process design, and provide a basis for process measurement requirements to be supported by an information system.

The contribution of the paper is both theoretical and practical. It provides clear‐cut ontology‐based definitions to concepts which so far have been assigned fuzzy and ambiguous meaning and uses these definitions for systematically tying business measures to business processes.

This chapter presents an analysis of the constitutional definitions of the right to education. Four countries of the Southern Common Market are selected: Argentina, Brazil, Uruguay (member countries) and Chile (an associate State). A conceptual definition of the right to education -from the human rights-based perspective- is provided in order to analyze, from a comparative standpoint, the constitutional norms of each country. In recent decades, these countries have experienced recurrent school reforms which, as they are framed within legal definitions, have regulated the right to education as a premise for overcoming social inequalities. First, a definition of this concept is provided. Second, the national constitutions of each country are analyzed so as to identify the definitions they have in this field. Subsequently, a comparative discussion of the underlying regulatory definitions of the right to education is carried out. Finally, the scope and limitations that the constitutional texts of these countries have on the right to education are discussed, which allow for a better understanding of school reform processes that were carried out during the last decades and that had the right to education as an object of regulation and reconfiguration.

In this paper, we investigate the process by which social control agents define wrongdoing over time and the principles they employ in drawing the boundary between right and wrong. We empirically examine how Italian state actors sought over four decades to categorize behaviors in the so-called “gray area,” i.e., the conduct of individuals supportive of the mafia organization Cosa Nostra and its criminal aims, but not members of the organization. Based on an archival analysis of texts produced since the 1960s, we reconstruct how state actors started from a preliminary definition of wrongdoing, moved to stigmatize the behaviors in question on moral grounds, and ultimately criminalized them with legal sanctions. We conceptualize the main principles behind this evolving categorization as intentionality of conduct, freedom of choice, and scope of harm. The paper contributes to the debate on the factors and conditions shaping the definition of wrongdoing over time and the contribution that social control agents provide to this aim.

Reactive rules are used for programming rule-based Web agents, which have the ability to detect events and respond to them automatically and can have complex structure and unpredictable behavior. The aim of this paper is to provide an appropriate formal framework for analyzing such rules.

To achieve this goal, the authors give two alternative semantics for the basic reactive rules’ families which allow us to specify reactive rule-based agents and verify their intended behavior. The first approach expresses the functionality of production and event condition action rules in terms of equations, whereas the second methodology is based in the formalism of rewriting logic. Both semantics can be expressed within the framework of CafeOBJ algebraic specification language, which then offers the verification support and have their advantages and downsides.

The authors report on experiences gained by applying those methodologies in a reactive rule-based system and compare the two methodologies.

Finally, the authors demonstrate a tool that translates a set of reactive rules into CafeOBJ rewrite rules, thus making the verification of reactive rules possible for inexperienced users.

The purpose of this paper is to compare gang member identification methods across regions in the United States as reported by law enforcement.

The data were collected through surveys with various law enforcement jurisdictions in both urban and rural communities across the United States. Methods of gang member identification were compared across the United States. Region through the use of Ordinal Logistic Regression and Multiple Imputation.

The results reveal that there are systematic variations in methods of gang member identification across regions in the United States. Specifically, the West is significantly more likely to identify gang members through associations or arrests with known gang members, symbols and self-nomination compared to other regions. The South, Northeast and Midwest regions are significantly more likely to identify gang members through a reliable informant compared to the West.

Research has not compared gang member identification methods across region in the United States or examined how variations in gang member identification methods potentially impact the accuracy of reported gang problems and prevalence.

This article argues that confusion exists as to exactly what constitutes a public-private partnership (P3). This confusion, it is maintained, creates problems for public procurement professionals when advising elected officials and government administrators on the appropriate uses of P3s. The article looks first at the imprecise language used by organizations (governments and others) to define, describe and discuss P3s. A proposed consensus definition of P3s is then introduced together with an accompanying proposed taxonomy of P3 types. The article then demonstrates how the proposed consensus definition and taxonomy can bring more clarity to discussions about P3s and their uses. The article concludes by suggesting that some public procurement standard setting organization should undertake the task of developing and promulgating more prescriptive guidance on P3s.