The Laggard Leader: A Historiography of the Origins of Wage and Income Inequality in the United States, 1973-84

Since 1973, wage and income inequality has increased dramatically in the United States. Workers who entered the labor market after the 1970s did not experience the same level of economic security as workers in the 1950s and 1960s during the “Golden Age of Capitalism.” Jobs paid relatively lower wages, there was less opportunity for collective bargaining, and fewer jobs offered healthcare coverage and pensions. When earnings increased after 1973, the gains disproportionately accrued to the top earners of the income distribution while workers at the bottom experienced stagnant and declining real incomes. What economic factors during the 1970s created a distinct shift towards rising income inequality? Lacking consensus among scholars, this work historiographically analyzes various explanations put forth by economic historians and labor economists regarding the origins of income inequality in the United States

Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP

Reprezentacija roda u Modernoj obitelji

Television reflects and shapes, or in Hall’s terms, represents American culture to the audience, and sitcoms are one of the major instruments through which dominant ideologies about gender are circulated. The aim of this thesis is to analyze how a popular American ABC’s sitcom, Modern Family, which apparently resists traditional gender portrayal, uses systems of representation to construct meanings and reproduce knowledge about gender, using mainly Stuart Hall’s work of representation and Foucault’s concepts of discourse, power, knowledge and normalization. The analysis shows that although Modern Family engages in social change, where characters and the way they interact do resist traditional gender discourses, the show still relies on stereotypes and patriarchic discourses of gender and family dynamics. Moreover, the normalizing power in Modern family is realized through stereotypical representations of difference, positioning as a butt of humor and subordinating everyone who does not fit to the norm, dividing between the normal and the abnormal, insiders and outsiders, which further reinforces and naturalizes traditional discourses of gender, sexuality and race.Televizija predstavlja i oblikuje, odnosno jezikom Stuarta Halla, reprezentira američku kulturu, a komedije situacije jedne su od glavnih instrumenata kroz koje cirkuliraju dominantne rodne ideologije. Cilj ovog rada jest analizirati kako popularna američka ABC-jeva komedija situacije, Moderna obitelj, koja se naizgled opire tradicionalnom prikazu roda, koristi specifične oblike reprezentacije kako bi konstruirala i cirkulirala rodna značenja i znanja, pri čemu će se uglavnom koristiti Hallov rad o reprezentaciji te Foucaultovi koncepti diskursa, znanja, moći i normalizacije. Analizom se pokazalo kako, iako Moderna obitelj koketira s društvenim promjenama, gdje se kroz likove i njihovu interakciju opire tradicionalnim diskursima o rodu, serija se i dalje oslanja na stereotipe i patrijarhalne diskurse roda i obiteljske dinamike. Također, moć normalizacije u Modernoj obitelji realizirana je kroz stereotipnu reprezentaciju različitosti, tj. drugosti, subordiniranje i pozicioniranje onih likova koji se opiru rodnim normama kao objekata humora, raščlanjivanjem između „normalnih“ i „nenormalnih“, „insidera“ i „outsidera“, što u konačnici dodatno učvršćuje i naturalizira tradicionalne diskurse o rodu, seksualnosti i rasi

“A volte ritornano”: il cronotopo e la serialità audiovisiva. Tra approcci analitici e casi di studio.

La connotazione temporale si pone come tratto caratterizzante della serialità e non sono mancate le proposte analitiche che hanno riattualizzato la pregnanza della categoria e dell'operatività del cronotopo. Dopo una prima parte in cui viene esplorata l'attivazione del cronotopo tramite un rinvio a Bachtin, e prendendo spunto dalla ripartizione che Angela Maiello propone nel suo testo "Mondi in Serie. L'epoca postmediale delle serie tv", la tesi è volta a individuare e riportare lo studio di diversi casi di serialità audiovisiva in cui sono analizzati i loro spazi saturi di temporalità

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

University of Michigan Law School Annual Report of Giving

An annual report of giving from the members of the University of Michigan Law School community covering July 1, 2015 - June 30, 2016

Vocal accommodation in human-computer interaction : modeling and integration into spoken dialogue systems

With the rapidly increasing usage of voice-activated devices worldwide, verbal communication with computers is steadily becoming more common. Although speech is the principal natural manner of human communication, it is still challenging for computers, and users had been growing accustomed to adjusting their speaking style for computers. Such adjustments occur naturally, and typically unconsciously, in humans during an exchange to control the social distance between the interlocutors and improve the conversation’s efficiency. This phenomenon is called accommodation and it occurs on various modalities in human communication, like hand gestures, facial expressions, eye gaze, lexical and grammatical choices, and others. Vocal accommodation deals with phonetic-level changes occurring in segmental and suprasegmental features. A decrease in the difference between the speakers’ feature realizations results in convergence, while an increasing distance leads to divergence. The lack of such mutual adjustments made naturally by humans in computers’ speech creates a gap between human-human and human-computer interactions. Moreover, voice-activated systems currently speak in exactly the same manner to all users, regardless of their speech characteristics or realizations of specific features. Detecting phonetic variations and generating adaptive speech output would enhance user personalization, offer more human-like communication, and ultimately should improve the overall interaction experience. Thus, investigating these aspects of accommodation will help to understand and improving human-computer interaction. This thesis provides a comprehensive overview of the required building blocks for a roadmap toward the integration of accommodation capabilities into spoken dialogue systems. These include conducting human-human and human-computer interaction experiments to examine the differences in vocal behaviors, approaches for modeling these empirical findings, methods for introducing phonetic variations in synthesized speech, and a way to combine all these components into an accommodative system. While each component is a wide research field by itself, they depend on each other and hence should be jointly considered. The overarching goal of this thesis is therefore not only to show how each of the aspects can be further developed, but also to demonstrate and motivate the connections between them. A special emphasis is put throughout the thesis on the importance of the temporal aspect of accommodation. Humans constantly change their speech over the course of a conversation. Therefore, accommodation processes should be treated as continuous, dynamic phenomena. Measuring differences in a few discrete points, e.g., beginning and end of an interaction, may leave many accommodation events undiscovered or overly smoothed. To justify the effort of introducing accommodation in computers, it should first be proven that humans even show any phonetic adjustments when talking to a computer as they do with a human being. As there is no definitive metric for measuring accommodation and evaluating its quality, it is important to empirically study humans productions to later use as references for possible behaviors. In this work, this investigation encapsulates different experimental configurations to achieve a better picture of accommodation effects. First, vocal accommodation was inspected where it naturally occurs, namely in spontaneous human-human conversations. For this purpose, a collection of real-world sales conversations, each with a different representative-prospect pair, was collected and analyzed. These conversations offer a glance into accommodation effects in authentic, unscripted interactions with the common goal of negotiating a deal on the one hand, but with the individual facet of each side of trying to get the best terms on the other hand. The conversations were analyzed using cross-correlation and time series techniques to capture the change dynamics over time. It was found that successful conversations are distinguishable from failed ones by multiple measures. Furthermore, the sales representative proved to be better at leading the vocal changes, i.e., making the prospect follow their speech styles rather than the other way around. They also showed a stronger tendency to take that lead at an earlier stage, all the more so in successful conversations. The fact that accommodation occurs more by trained speakers and improves their performances fits anecdotal best practices of sales experts, which are now also proven scientifically. Following these results, the next experiment came closer to the final goal of this work and investigated vocal accommodation effects in human-computer interaction. This was done via a shadowing experiment, which offers a controlled setting for examining phonetic variations. As spoken dialogue systems with such accommodation capabilities (like this work aims to achieve) do not exist yet, a simulated system was used to introduce these changes to the participants, who believed they help with the testing of a language learning tutoring system. After determining their preference concerning three segmental phonetic features, participants were listen-ing to either natural or synthesized voices of male and female speakers, which produced the participants’ dispreferred variation of the aforementioned features. Accommodation occurred in all cases, but the natural voices triggered stronger effects. Nevertheless, it can be concluded that participants were accommodating toward synthetic voices as well, which means that social mechanisms are applied in humans also when speaking with computer-based interlocutors. The shadowing paradigm was utilized also to test whether accommodation is a phenomenon associated only with speech or with other vocal productions as well. To that end, accommodation in the singing of familiar and novel music was examined. Interestingly, accommodation was found in both cases, though in different ways. While participants seemed to use the familiar piece merely as a reference for singing more accurately, the novel piece became the goal for complete replicate. For example, one difference was that mostly pitch corrections were introduced in the former case, while in the latter also key and rhythmic patterns were adopted. Some of those findings were expected and they show that people’s more salient features are also harder to modify using external auditory influence. Lastly, a multiparty experiment with spontaneous human-human-computer interactions was carried out to compare accommodation in human-directed and computer-directed speech. The participants solved tasks for which they needed to talk both with a confederate and with an agent. This allows a direct comparison of their speech based on the addressee within the same conversation, which has not been done so far. Results show that some participants’ vocal behavior changed similarly when talking to the confederate and the agent, while others’ speech varied only with the confederate. Further analysis found that the greatest factor for this difference was the order in which the participants talked with the interlocutors. Apparently, those who first talked to the agent alone saw it more as a social actor in the conversation, while those who interacted with it after talking to the confederate treated it more as a means to achieve a goal, and thus behaved differently with it. In the latter case, the variations in the human-directed speech were much more prominent. Differences were also found between the analyzed features, but the task type did not influence the degree of accommodation effects. The results of these experiments lead to the conclusion that vocal accommodation does occur in human-computer interactions, even if often to lesser degrees. With the question of whether people accommodate to computer-based interlocutors as well answered, the next step would be to describe accommodative behaviors in a computer-processable manner. Two approaches are proposed here: computational and statistical. The computational model aims to capture the presumed cognitive process associated with accommodation in humans. This comprises various steps, such as detecting the variable feature’s sound, adding instances of it to the feature’s mental memory, and determining how much the sound will change while taking into account both its current representation and the external input. Due to its sequential nature, this model was implemented as a pipeline. Each of the pipeline’s five steps corresponds to a specific part of the cognitive process and can have one or more parameters to control its output (e.g., the size of the feature’s memory or the accommodation pace). Using these parameters, precise accommodative behaviors can be crafted while applying expert knowledge to motivate the chosen parameter values. These advantages make this approach suitable for experimentation with pre-defined, deterministic behaviors where each step can be changed individually. Ultimately, this approach makes a system vocally responsive to users’ speech input. The second approach grants more evolved behaviors, by defining different core behaviors and adding non-deterministic variations on top of them. This resembles human behavioral patterns, as each person has a base way of accommodating (or not accommodating), which may arbitrarily change based on the specific circumstances. This approach offers a data-driven statistical way to extract accommodation behaviors from a given collection of interactions. First, the target feature’s values of each speaker in an interaction are converted into continuous interpolated lines by drawing one sample from the posterior distribution of a Gaussian process conditioned on the given values. Then, the gradients of these lines, which represent rates of mutual change, are used to defined discrete levels of change based on their distribution. Finally, each level is assigned a symbol, which ultimately creates a symbol sequence representation for each interaction. The sequences are clustered so that each cluster stands for a type of behavior. The sequences of a cluster can then be used to calculate n-gram probabilities that enable the generation of new sequences of the captured behavior. The specific output value is sampled from the range corresponding to the generated symbol. With this approach, accommodation behaviors are extracted directly from data, as opposed to manually crafting them. However, it is harder to describe what exactly these behaviors represent and motivate the use of one of them over the other. To bridge this gap between these two approaches, it is also discussed how they can be combined to benefit from the advantages of both. Furthermore, to generate more structured behaviors, a hierarchy of accommodation complexity levels is suggested here, from a direct adoption of users’ realizations, via specified responsiveness, and up to independent core behaviors with non-deterministic variational productions. Besides a way to track and represent vocal changes, an accommodative system also needs a text-to-speech component that is able to realize those changes in the system’s speech output. Speech synthesis models are typically trained once on data with certain characteristics and do not change afterward. This prevents such models from introducing any variation in specific sounds and other phonetic features. Two methods for directly modifying such features are explored here. The first is based on signal modifications applied to the output signal after it was generated by the system. The processing is done between the timestamps of the target features and uses pre-defined scripts that modify the signal to achieve the desired values. This method is more suitable for continuous features like vowel quality, especially in the case of subtle changes that do not necessarily lead to a categorical sound change. The second method aims to capture phonetic variations in the training data. To that end, a training corpus with phonemic representations is used, as opposed to the regular graphemic representations. This way, the model can learn more direct relations between phonemes and sound instead of surface forms and sound, which, depending on the language, might be more complex and depend on their surrounding letters. The target variations themselves don’t necessarily need to be explicitly present in the training data, all time the different sounds are naturally distinguishable. In generation time, the current target feature’s state determines the phoneme to use for generating the desired sound. This method is suitable for categorical changes, especially for contrasts that naturally exist in the language. While both methods have certain limitations, they provide a proof of concept for the idea that spoken dialogue systems may phonetically adapt their speech output in real-time and without re-training their text-to-speech models. To combine the behavior definitions and the speech manipulations, a system is required, which can connect these elements to create a complete accommodation capability. The architecture suggested here extends the standard spoken dialogue system with an additional module, which receives the transcribed speech signal from the speech recognition component without influencing the input to the language understanding component. While language the understanding component uses only textual transcription to determine the user’s intention, the added component process the raw signal along with its phonetic transcription. In this extended architecture, the accommodation model is activated in the added module and the information required for speech manipulation is sent to the text-to-speech component. However, the text-to-speech component now has two inputs, viz. the content of the system’s response coming from the language generation component and the states of the defined target features from the added component. An implementation of a web-based system with this architecture is introduced here, and its functionality is showcased by demonstrating how it can be used to conduct a shadowing experiment automatically. This has two main advantage: First, since the system recognizes the participants’ phonetic variations and automatically selects the appropriate variation to use in its response, the experimenter saves time and prevents manual annotation errors. The experimenter also automatically gains additional information, like exact timestamps of utterances, real-time visualization of the interlocutors’ productions, and the possibility to replay and analyze the interaction after the experiment is finished. The second advantage is scalability. Multiple instances of the system can run on a server and be accessed by multiple clients at the same time. This not only saves time and the logistics of bringing participants into a lab, but also allows running the experiment with different configurations (e.g., other parameter values or target features) in a controlled and reproducible way. This completes a full cycle from examining human behaviors to integrating accommodation capabilities. Though each part of it can undoubtedly be further investigated, the emphasis here is on how they depend and connect to each other. Measuring changes features without showing how they can be modeled or achieving flexible speech synthesis without considering the desired final output might not lead to the final goal of introducing accommodation capabilities into computers. Treating accommodation in human-computer interaction as one large process rather than isolated sub-problems lays the ground for more comprehensive and complete solutions in the future.Heutzutage wird die verbale Interaktion mit Computern immer gebräuchlicher, was der rasant wachsenden Anzahl von sprachaktivierten Geräten weltweit geschuldet ist. Allerdings stellt die computerseitige Handhabung gesprochener Sprache weiterhin eine große Herausforderung dar, obwohl sie die bevorzugte Art zwischenmenschlicher Kommunikation repräsentiert. Dieser Umstand führt auch dazu, dass Benutzer ihren Sprachstil an das jeweilige Gerät anpassen, um diese Handhabung zu erleichtern. Solche Anpassungen kommen in menschlicher gesprochener Sprache auch in der zwischenmenschlichen Kommunikation vor. Üblicherweise ereignen sie sich unbewusst und auf natürliche Weise während eines Gesprächs, etwa um die soziale Distanz zwischen den Gesprächsteilnehmern zu kontrollieren oder um die Effizienz des Gesprächs zu verbessern. Dieses Phänomen wird als Akkommodation bezeichnet und findet auf verschiedene Weise während menschlicher Kommunikation statt. Sie äußert sich zum Beispiel in der Gestik, Mimik, Blickrichtung oder aber auch in der Wortwahl und dem verwendeten Satzbau. Vokal- Akkommodation beschäftigt sich mit derartigen Anpassungen auf phonetischer Ebene, die sich in segmentalen und suprasegmentalen Merkmalen zeigen. Werden Ausprägungen dieser Merkmale bei den Gesprächsteilnehmern im Laufe des Gesprächs ähnlicher, spricht man von Konvergenz, vergrößern sich allerdings die Unterschiede, so wird dies als Divergenz bezeichnet. Dieser natürliche gegenseitige Anpassungsvorgang fehlt jedoch auf der Seite des Computers, was zu einer Lücke in der Mensch-Maschine-Interaktion führt. Darüber hinaus verwenden sprachaktivierte Systeme immer dieselbe Sprachausgabe und ignorieren folglich etwaige Unterschiede zum Sprachstil des momentanen Benutzers. Die Erkennung dieser phonetischen Abweichungen und die Erstellung von anpassungsfähiger Sprachausgabe würden zur Personalisierung dieser Systeme beitragen und könnten letztendlich die insgesamte Benutzererfahrung verbessern. Aus diesem Grund kann die Erforschung dieser Aspekte von Akkommodation helfen, Mensch-Maschine-Interaktion besser zu verstehen und weiterzuentwickeln. Die vorliegende Dissertation stellt einen umfassenden Überblick zu Bausteinen bereit, die nötig sind, um Akkommodationsfähigkeiten in Sprachdialogsysteme zu integrieren. In diesem Zusammenhang wurden auch interaktive Mensch-Mensch- und Mensch- Maschine-Experimente durchgeführt. In diesen Experimenten wurden Differenzen der vokalen Verhaltensweisen untersucht und Methoden erforscht, wie phonetische Abweichungen in synthetische Sprachausgabe integriert werden können. Um die erhaltenen Ergebnisse empirisch auswerten zu können, wurden hierbei auch verschiedene Modellierungsansätze erforscht. Fernerhin wurde der Frage nachgegangen, wie sich die betreffenden Komponenten kombinieren lassen, um ein Akkommodationssystem zu konstruieren. Jeder dieser Aspekte stellt für sich genommen bereits einen überaus breiten Forschungsbereich dar. Allerdings sind sie voneinander abhängig und sollten zusammen betrachtet werden. Aus diesem Grund liegt ein übergreifender Schwerpunkt dieser Dissertation darauf, nicht nur aufzuzeigen, wie sich diese Aspekte weiterentwickeln lassen, sondern auch zu motivieren, wie sie zusammenhängen. Ein weiterer Schwerpunkt dieser Arbeit befasst sich mit der zeitlichen Komponente des Akkommodationsprozesses, was auf der Beobachtung fußt, dass Menschen im Laufe eines Gesprächs ständig ihren Sprachstil ändern. Diese Beobachtung legt nahe, derartige Prozesse als kontinuierliche und dynamische Prozesse anzusehen. Fasst man jedoch diesen Prozess als diskret auf und betrachtet z.B. nur den Beginn und das Ende einer Interaktion, kann dies dazu führen, dass viele Akkommodationsereignisse unentdeckt bleiben oder übermäßig geglättet werden. Um die Entwicklung eines vokalen Akkommodationssystems zu rechtfertigen, muss zuerst bewiesen werden, dass Menschen bei der vokalen Interaktion mit einem Computer ein ähnliches Anpassungsverhalten zeigen wie bei der Interaktion mit einem Menschen. Da es keine eindeutig festgelegte Metrik für das Messen des Akkommodationsgrades und für die Evaluierung der Akkommodationsqualität gibt, ist es besonders wichtig, die Sprachproduktion von Menschen empirisch zu untersuchen, um sie als Referenz für mögliche Verhaltensweisen anzuwenden. In dieser Arbeit schließt diese Untersuchung verschiedene experimentelle Anordnungen ein, um einen besseren Überblick über Akkommodationseffekte zu erhalten. In einer ersten Studie wurde die vokale Akkommodation in einer Umgebung untersucht, in der sie natürlich vorkommt: in einem spontanen Mensch-Mensch Gespräch. Zu diesem Zweck wurde eine Sammlung von echten Verkaufsgesprächen gesammelt und analysiert, wobei in jedem dieser Gespräche ein anderes Handelsvertreter-Neukunde Paar teilgenommen hatte. Diese Gespräche verschaffen einen Einblick in Akkommodationseffekte während spontanen authentischen Interaktionen, wobei die Gesprächsteilnehmer zwei Ziele verfolgen: zum einen soll ein Geschäft verhandelt werden, zum anderen möchte aber jeder Teilnehmer für sich die besten Bedingungen aushandeln. Die Konversationen wurde durch das Kreuzkorrelation-Zeitreihen-Verfahren analysiert, um die dynamischen Änderungen im Zeitverlauf zu erfassen. Hierbei kam zum Vorschein, dass sich erfolgreiche Konversationen von fehlgeschlagenen Gesprächen deutlich unterscheiden lassen. Überdies wurde festgestellt, dass die Handelsvertreter die treibende Kraft von vokalen Änderungen sind, d.h. sie können die Neukunden eher dazu zu bringen, ihren Sprachstil anzupassen, als andersherum. Es wurde auch beobachtet, dass sie diese Akkommodation oft schon zu einem frühen Zeitpunkt auslösen, was besonders bei erfolgreichen Gesprächen beobachtet werden konnte. Dass diese Akkommodation stärker bei trainierten Sprechern ausgelöst wird, deckt sich mit den meist anekdotischen Empfehlungen von erfahrenen Handelsvertretern, die bisher nie wissenschaftlich nachgewiesen worden sind. Basierend auf diesen Ergebnissen beschäfti

Victors for Michigan: Honor Roll of Donors

An annual report of giving from the members of the University of Michigan Law School community covering July 1, 2016 - June 30, 2017