The Law and Economics of Critical Race Theory

Legal academics often perceive law and economics (L&E) and critical race theory (CRT) as oppositional discourses. Using a recently published collection of essays on CRT as a starting point, we argue that the understanding of workplace discrimination can be furthered through a collaboration between L&E and CRT. L&E\u27s strength is in its attention to incentives and norms, specifically its concern with explicating how norms incentivize behavior. Its limitation is that it treats race as exogenous and static. Thus, the literature fails to consider how institutional norms affect, and are affected by, race. To put the point another way, L&E does not discuss how norms incentivize racial behavior, obscuring that how people present their race (or themselves as racial subjects) is a function of norms. The strength of CRT is its conception of race as a social construction. Under this view, race is neither biologically determined nor fixed. Instead, race is ever evolving as a function of social, political, legal, and economic pressures. A limitation of CRT is that much of its analysis of race as a social construction is macro-oriented. Thus, CRT has paid insufficient attention to the social construction of race within specific institutional settings, like the workplace. Further, CRT has virtually ignored the agency people of color exercise to shape how their racial identity is interpreted - that is say, constructed. Explicitly incorporating L&E\u27s focus on incentives and norms into CRT provides CRT with a means by which to articulate the notion of race as a social construction at the level of individual choice. The basic idea is that people of color construct (present racial impressions of) themselves in response to norms. Norms, in this sense, are racially productive, and individuals are part of the production apparatus. Having set out the basic elements of the collaborative enterprise, we deploy this collaboration to respond to a specific and important question about the workplace: How are modern employers and employees likely to manage workplace racial diversity? We raise this question because we assume that, for institutional legitimacy reasons, most workplaces will strive to achieve at least a modicum of racial diversity. The question, again, is: How will this diversity be managed? Part of the answer has to do with assimilation, an ideological technology for constructing race and a central theme in CRT; and part of the answer has to do with efficiency, an ideological technology for creating incentives and a central theme in L&E. Both ideas - assimilation and efficiency - combine to tell a story about workplace discrimination that derives from what we call the homogeneity incentive. In sum, in order to increase efficiency, employers have incentives to screen prospective employees for homogeneity, and, in order to counter racial stereotypes, nonwhite employees have incentives to demonstrate a willingness and capacity to assimilate. In this sense, the modern workplace discrimination problem may be more about employers requiring people of color to demonstrate racial palatability than about employers totally excluding people of color for the workplace. We discuss whether and to what extent anti-discrimination law can ameliorate this problem

The Law and Economics of Critical Race Theory

Legal academics often perceive law and economics (L&E) and critical race theory (CRT) as oppositional discourses. Using a recently published collection of essays on CRT as a starting point, we argue that the understanding of workplace discrimination can be furthered through a collaboration between L&E and CRT. L&E\u27s strength is in its attention to incentives and norms, specifically its concern with explicating how norms incentivize behavior. Its limitation is that it treats race as exogenous and static. Thus, the literature fails to consider how institutional norms affect, and are affected by, race. To put the point another way, L&E does not discuss how norms incentivize racial behavior, obscuring that how people present their race (or themselves as racial subjects) is a function of norms. The strength of CRT is its conception of race as a social construction. Under this view, race is neither biologically determined nor fixed. Instead, race is ever evolving as a function of social, political, legal, and economic pressures. A limitation of CRT is that much of its analysis of race as a social construction is macro-oriented. Thus, CRT has paid insufficient attention to the social construction of race within specific institutional settings, like the workplace. Further, CRT has virtually ignored the agency people of color exercise to shape how their racial identity is interpreted - that is say, constructed. Explicitly incorporating L&E\u27s focus on incentives and norms into CRT provides CRT with a means by which to articulate the notion of race as a social construction at the level of individual choice. The basic idea is that people of color construct (present racial impressions of) themselves in response to norms. Norms, in this sense, are racially productive, and individuals are part of the production apparatus. Having set out the basic elements of the collaborative enterprise, we deploy this collaboration to respond to a specific and important question about the workplace: How are modern employers and employees likely to manage workplace racial diversity? We raise this question because we assume that, for institutional legitimacy reasons, most workplaces will strive to achieve at least a modicum of racial diversity. The question, again, is: How will this diversity be managed? Part of the answer has to do with assimilation, an ideological technology for constructing race and a central theme in CRT; and part of the answer has to do with efficiency, an ideological technology for creating incentives and a central theme in L&E. Both ideas - assimilation and efficiency - combine to tell a story about workplace discrimination that derives from what we call the homogeneity incentive. In sum, in order to increase efficiency, employers have incentives to screen prospective employees for homogeneity, and, in order to counter racial stereotypes, nonwhite employees have incentives to demonstrate a willingness and capacity to assimilate. In this sense, the modern workplace discrimination problem may be more about employers requiring people of color to demonstrate racial palatability than about employers totally excluding people of color for the workplace. We discuss whether and to what extent anti-discrimination law can ameliorate this problem

Active Object Localization in Visual Situations

We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting for a bus"---whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. Our system combines given and learned knowledge of the structure of a particular situation, and adapts that knowledge to a new situation instance as it actively searches for objects. More specifically, the system learns a set of probability distributions describing spatial and other relationships among relevant objects. The system uses those distributions to iteratively sample object proposals on a test image, but also continually uses information from those object proposals to adaptively modify the distributions based on what the system has detected. We test our approach's ability to efficiently localize objects, using a situation-specific image dataset created by our group. We compare the results with several baselines and variations on our method, and demonstrate the strong benefit of using situation knowledge and active context-driven localization. Finally, we contrast our method with several other approaches that use context as well as active search for object localization in images.Comment: 14 page

School-aged children learning second language sounds: The Effects of different learning backgrounds on children’s second language sound production and perception learning

Earlier phonetic research on children’s second language sound learning has primarily focused on naturalistic language learning environments and the comparison of child and adult learners. The aim of this thesis was to examine how 6–13-year-old Finnish children with different learning background factors learn to perceive and produce second language sounds in instructed settings and phonetic training contexts. The background factors examined in Studies I–IV of this thesis were age, language immersion education and music-oriented education. The aim of Study I was to discover how passive auditory training affects 7–12-year-old children’s non-native sound perception. The results showed that the older participants’ (9–12 years) perceived the trained non-native sound after passive auditory training. Study II examined how studying in a language immersion program in elementary school affects 11–13-year-old children’s second language pronunciation. The findings suggest that studying in a language immersion program results in more accurate second language pronunciation when compared to nonimmersive learning. Study III investigated how music-oriented education in elementary school affects 9–11-year-old childen’s ability to learn non-native sound production through auditory training. No significant effects of music-oriented education were found. Study IV aimed to discover how listen-and-repeat training affects 6–7-year-old preschoolers’ non-native sound production. The results showed rapid changes in production after minimal amount of listen-and-repeat training. Overall, the results of Studies I–IV suggested that age of learning has the strongest effect on school-aged children’s second language sound learning.Kouluikäiset lapset oppimassa vieraan kielen äänteitä: Erilaisten oppimistaustojen vaikutus lasten vieraan kielen äänteiden tuoton ja havaitsemisen oppimiseen Aiemmat lasten vieraan kielen äänteiden oppimista selvittävät foneettiset tutkimukset ovat pitkälti keskittyneet luonnollisiin kielenoppimisympäristöihin sekä lapsi- ja aikuisoppijoiden vertailuun. Tämän tutkielman tavoite oli selvittää, kuinka erilaiset oppimisen taustatekijät vaikuttavat 6–13-vuotiaiden suomenkielisten lasten vieraan kielen äänteiden havaitsemisen ja tuoton oppimiseen. Tarkasteltavat taustatekijät olivat ikä ja kieli- tai musiikkiluokalla opiskelu. Tutkimus koostui neljästä osatutkimuksesta. Tutkimuksessa I selvitettiin passiivisen auditorisen treenin vaikutuksia 7–12-vuotiaiden lasten vieraan kielen äänteen havaitsemiseen. Tulokset osoittivat, että vanhemmat (9–12 vuotta) osallistujat havaitsivat treenatun vieraan kielen äänteen passiivisen kuuntelun jälkeen. Tutkimuksessa II tarkasteltiin peruskoulun kieliluokalla opiskelun vaikutuksia 11–13-vuotiaiden lasten vieraan kielen ääntämiseen. Tulokset viittasivat, että kieliluokalla opiskelu johtaa parempaan ääntämistarkkuuteen kuin perinteinen luokkahuoneoppiminen. Tutkimuksen III tavoite puolestaan oli selvittää, vaikuttaako musiikkiluokalla opiskelu 9–11-vuotiaiden lasten kykyyn oppia tuottamaan vieraan kielen äännekontrasti auditorisen treenin avulla. Musiikkiluokalla opiskeluun liittyviä merkitseviä eroja tuoton oppimisessa ei löydetty. Tutkimuksessa IV selvitettiin, kuinka kuuntele-ja-toista treeni vaikuttaa 6–7-vuotiaiden esikoululaisten vieraan kielen äännekontrastin tuottoon. Tulokset osoittivat, että esikoululaiset muuttivat tuottoaan nopeasti vähäisen harjoittelun jälkeen. Kokonaisuudessaan tutkimuksen tulokset antoivat viitteitä siitä, että oppimisikä vaikuttaa kouluikäisten lasten vieraan kielen äänteiden oppimiseen voimakkaammin kuin muut tarkastellut taustatekijät

Hypothesis-based image segmentation for object learning and recognition

Denecke A. Hypothesis-based image segmentation for object learning and recognition. Bielefeld: Universität Bielefeld; 2010.This thesis addresses the figure-ground segmentation problem in the context of complex systems for automatic object recognition as well as for the online and interactive acquisition of visual representations. First the problem of image segmentation in general terms and next its importance for object learning in current state-of-the-art systems is introduced. Secondly a method using artificial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time figure-ground segmentation of complex shaped objects under continuously changing environment conditions. The ability to fulfill these requirements characterizes the novelty of the approach compared to state-of-the-art methods. Finally our technique is extended towards online adaption of model complexity and the integration of several segmentation cues. This yields a framework for object segmentation that is applicable to improve current systems for visual object learning and recognition

Data exploration process based on the self-organizing map

With the advances in computer technology, the amount of data that is obtained from various sources and stored in electronic media is growing at exponential rates. Data mining is a research area which answers to the challange of analysing this data in order to find useful information contained therein. The Self-Organizing Map (SOM) is one of the methods used in data mining. It quantizes the training data into a representative set of prototype vectors and maps them on a low-dimensional grid. The SOM is a prominent tool in the initial exploratory phase in data mining. The thesis consists of an introduction and ten publications. In the publications, the validity of SOM-based data exploration methods has been investigated and various enhancements to them have been proposed. In the introduction, these methods are presented as parts of the data mining process, and they are compared with other data exploration methods with similar aims. The work makes two primary contributions. Firstly, it has been shown that the SOM provides a versatile platform on top of which various data exploration methods can be efficiently constructed. New methods and measures for visualization of data, clustering, cluster characterization, and quantization have been proposed. The SOM algorithm and the proposed methods and measures have been implemented as a set of Matlab routines in the SOM Toolbox software library. Secondly, a framework for SOM-based data exploration of table-format data - both single tables and hierarchically organized tables - has been constructed. The framework divides exploratory data analysis into several sub-tasks, most notably the analysis of samples and the analysis of variables. The analysis methods are applied autonomously and their results are provided in a report describing the most important properties of the data manifold. In such a framework, the attention of the data miner can be directed more towards the actual data exploration task, rather than on the application of the analysis methods. Because of the highly iterative nature of the data exploration, the automation of routine analysis tasks can reduce the time needed by the data exploration process considerably.reviewe