Veteranness : Representations of Combat-related PTSD in U.S. Popular Visual Media

Posttraumatic stress and PTSD are becoming familiar terms to refer to what we often call the invisible wounds of war, yet these are recent additions to a popular discourse in which images of and ideas about combat-affected veterans have long circulated. A legacy of ideas about combat veterans and war trauma thus intersects with more recent clinical information about PTSD to become part of a discourse of visual media that has defined and continues to redefine veteran for popular audiences. In this dissertation I examine realist combat veteran representations in selected films and other visual media from three periods: during and after World Wars I and II (James Allen from I Am a Fugitive from a Chain Gang, Fred Derry and Al Stephenson from The Best Years of Our Lives); after the Vietnam War (Michael from The Deer Hunter, Eriksson from Casualties of War), and post 9/11 (Will James from The Hurt Locker, a collection of veterans from Wartorn: 1861-2010.) Employing a theoretical framework informed by visual media studies, Barthes’ concept of myth, and Foucault’s concept ofdiscursive unity, I analyze how these veteran representations are endowed with PTSD symptom-like behaviors and responses that seem reasonable and natural within the narrative arc. I contend that veteran myths appear through each veteran representation as the narrative develops and resolves. I argue that these veteran myths are many and varied but that they crystallize in a dominant veteran discourse, a discursive unity that I term veteranness. I further argue that veteranness entangles discrete categories such as veteran, combat veteran, and PTSD with veteran myths, often tying dominant discourse about combat-related PTSD to outdated or outmoded notions that significantly affect our attitudes about and treatment of veterans. A basic premise of my research is that unless and until we learn about the lasting effects of the trauma inherent to combat, we hinder our ability to fulfill our responsibilities to war veterans. A society that limits its understanding of posttraumatic stress, PTSD and post-war experiences of actual veterans affected by war trauma to veteranness or veteran myths risks normalizing or naturalizing an unexamined set of sociocultural expectations of all veterans, rendering them voice-less, invisible, and, ultimately disposable.

Towards flexible parsing of structured textual model representations

Existing parsers for textual model representation formats such as XMI and HUTN are unforgiving and fail upon even the smallest inconsistency between the structure and naming of metamodel elements and the contents of serialised models. In this paper, we demonstrate how a fuzzy parsing approach can transparently and automatically resolve a number of these inconsistencies, and how it can eventually turn XML into a human-readable and editable textual model representation format for particular classes of models.

Defending the 'public interest': an assessment of competing actor representations of 'solutions' to growing natural resource deficiencies.

This paper applies a SRT framework to the study of two case studies, namely the recent campaign of opposition to the legalization of hydraulic fracking in the State of New York and the more ongoing debate on land leasing in Africa. In relation to both campaigns, the analysis accounts for the arguments of a major financial institution and industry representatives who stress the safe and value-adding dimensions of these practices, as well as the views of opponents who refute the validity of industry's position and point to the unacceptable risks posed to the community, health and the environment. In spite of a number of obvious differences between these two case studies, not least differences arising from contrasting socio-economic and geo-political settings, there were also some notable similarities. First, was a tendency amongst protesters in both cases to formulate their role as contemporaries in a historically extended struggle for democratic justice. All perceived of themselves as guardians of their community's right to resist a corporate 'invasion' of their territories, like their forefathers and mothers before them. A theme of colonialism was explored in both settings through various identity and thematic anchoring devices that deliberately evoked shared understandings and historical memories of exploitation and human suffering. The evocation of powerful symbols of identity through visual narratives of protest further reinforced the cultural comprehensibility of opponents' message of protest in both contexts.

The potential of symbolic approximation. Disentangling the effects of approximation vs. calculation demands in nonsymbolic and symbolic representations.

Mathematical skills that we acquire during formal education mostly entail exact numerical processing. Besides this specifically human faculty, an additional system exists to represent and manipulate quantities in an approximate manner. We share this innate approximate number system (ANS) with other nonhuman animals and are able to use it to process large numerosities long before we can master the formal algorithms taught in school. Dehaene´s (1992) Triple Code Model (TCM) states that also after the onset of formal education, approximate processing is carried out in this analogue magnitude code no matter if the original problem was presented nonsymbolically or symbolically. Despite the wide acceptance of the model, most research only uses nonsymbolic tasks to assess ANS acuity. Due to this silent assumption that genuine approximation can only be tested with nonsymbolic presentations, up to now important implications in research domains of high practical relevance remain unclear, and existing potential is not fully exploited. For instance, it has been found that nonsymbolic approximation can predict math achievement one year later (Gilmore, McCarthy, & Spelke, 2010), that it is robust against the detrimental influence of learners´ socioeconomic status (SES), and that it is suited to foster performance in exact arithmetic in the short-term (Hyde, Khanum, & Spelke, 2014). We provided evidence that symbolic approximation might be equally and in some cases even better suited to generate predictions and foster more formal math skills independently of SES. In two longitudinal studies, we realized exact and approximate arithmetic tasks in both a nonsymbolic and a symbolic format. With first graders, we demonstrated that performance in symbolic approximation at the beginning of term was the only measure consistently not varying according to children´s SES, and among both approximate tasks it was the better predictor for math achievement at the end of first grade. In part, the strong connection seems to come about from mediation through ordinal skills. In two further experiments, we tested the suitability of both approximation formats to induce an arithmetic principle in elementary school children. We found that symbolic approximation was equally effective in making children exploit the additive law of commutativity in a subsequent formal task as a direct instruction. Nonsymbolic approximation on the other hand had no beneficial effect. The positive influence of the symbolic approximate induction was strongest in children just starting school and decreased with age. However, even third graders still profited from the induction. The results show that also symbolic problems can be processed as genuine approximation, but that beyond that they have their own specific value with regard to didactic-educational concerns. Our findings furthermore demonstrate that the two often con-founded factors ꞌformatꞌ and ꞌdemanded accuracyꞌ cannot be disentangled easily in first graders numerical understanding, but that children´s SES also influences existing interrelations between the different abilities tested here.

ESL teacher profiles of ICT integration in their classroom practices and assessment activities : a portrait viewed through the lens of some Quebec teachers’ social representations

Abstract : Information and communication technologies (ICTs, henceforth) have become ubiquitous in our society. The plethora of devices competing with the computer, from iPads to the Interactive whiteboard, just to name a few, has provided teachers and students alike with the ability to communicate and access information with unprecedented accessibility and speed. It is only logical that schools reflect these changes given that their purpose is to prepare students for the future. Surprisingly enough, research indicates that ICT integration into teaching activities is still marginal. Many elementary and secondary schoolteachers are not making effective use of ICTs in their teaching activities as well as in their assessment practices. The purpose of the current study is a) to describe Quebec ESL teachers’ profiles of using ICTs in their daily teaching activities; b) to describe teachers’ ICT integration and assessment practices; and c) to describe teachers’ social representations regarding the utility and relevance of ICT use in their daily teaching activities and assessment practices. In order to attain our objectives, we based our theoretical framework, principally, on the social representations (SR, henceforth) theory and we defined most related constructs which were deemed fundamental to the current thesis. We also collected data from 28 ESL elementary and secondary school teachers working in public and private sectors. The interview guide used to that end included a range of items to elicit teachers’ SR in terms of ICT daily use in teaching activities as well as in assessment practices. In addition, we carried out our data analyses from a textual statistics perspective, a particular mode of content analysis, in order to extract the indicators underlying teachers’ representations of the teachers. The findings suggest that although almost all participants use a wide range of ICT tools in their practices, ICT implementation is seemingly not exploited to its fullest potential and, correspondingly, is likely to produce limited effects on students’ learning. Moreover, none of the interviewees claim that they use ICTs in their assessment practices and they still hold to the traditional paper-based assessment (PBA, henceforth) approach of assessing students’ learning. Teachers’ common discourse reveals a gap between the positive standpoint with regards to ICT integration, on the one hand, and the actual uses of instructional technology, on the other. These results are useful for better understanding the way ESL teachers in Quebec currently view their use of ICTs, particularly for evaluation purposes. In fact, they provide a starting place for reconsidering the implementation of ICTs in elementary and secondary schools. They may also be useful to open up avenues for the development of a future research program in this regard.

Social representations about the Portuguese adolescent alcohol consumption

Alcohol is currently the most widely consumed psychoactive substance in the world and Portugal is the second country where such consumption is greater, registering a large increase in consumption by young people. Currently continue still, beliefs, myths and prejudices that because they are well rooted culturally serve as good reasons for drinking. This study sought therefore to identify the myths associated by adolescents to alcohol consumption. A questionnaire was developed for this purpose (74 items, α = 0.947) and applied to a sample of 1176 adolescents schooled between 14 and 18 years old, with a return rate of 42.6% (margin of error of 5% for a confidence level of 95%) in the district of Beja, Portugal, in 2012. The collected data were statistically analyzed using measures of association, factor analysis and linear regression. The results show that many myths are unknown among adolescents, verifying the presence of many questions, among which stands out: alcohol "warm", "thirst quenching", "gives strength", "facilitates digestion" "whet the appetite", "is a medicine", "is aphrodisiac", "facilitates social relations", among others. Age and sex are variables significantly affected the myths and objectives of alcohol consumption. These results clearly point to the need to be disassembled beliefs and wrong conceptions about the effects of alcohol consumption, particularly in the school environment, reducing the risk of the consequences and promoting adolescent health, preventing any future dependence on this psychoactive substance.

Social Representations of Violence against the Elderly

The repercussions of violence on the mental, social, and physical well-being of the elderly are some of the most challenging problems in public health today. Using a qualitative design, we conducted a study in Portugal and the United States that applied both descriptive and comparative methods in order to understand the social representations of violence against the elderly. Utilizing the Theory of Social Representations, we explored the perspectives of the elderly, their families, and healthcare professionals on the subject of violence against the elderly. The data on which the findings were based were obtained in two very different cultural contexts, yet the representations of violence against the elderly revealed no significant cross-cultural differences. However, conceptualizations regarding expectations of care and protection for the elderly proved to be distinct. We discussed concerns about the general attitudes of tolerance toward violence, including those of the elderly who self-identified as eventual victims. Violence against the elderly was portrayed as a part of old age and also somehow was justified by it. The results also indicated the need to better prepare healthcare professionals and society in general to deal with the consequences of the problem and not, as we would like to report, to prevent it from happening.

The Theory of Social Representations in Brazilian Health Researches: a Bibliometric Profile

The study aimed to characterizing the production of national articles on health, the time frame of the past 10 years, available in the database LILACS and MEDLINE Virtual Health Library that used the Theory of Social Representations in its searches, using as descriptors the words: social representations and health. It is a descriptive study, developed in the context of ibliometrics. Of the 158 units found, 122 were considered and analyzed after removal of those that did not include the stablished inclusion criteria: articles in Portuguese,available in full and that mentioned the expression "social representations", either in the title or abstract. The journal that most published researches about the Theory of Social Representations was Science & Public Health; being the largest number of articles published in 2011. The most frequent area of knowledge covering about the Theory of Social Representations was the Public Health, with the participant group most cited health professionals. Among the data collection instruments used, the semi-structured interview was the most frequent and the kind of qualitative analysis the content analysis was the most common. Noteworthy is the growing interest for the theory and the need for greater criteria in the preparation of abstracts, considering its importance in the spread of scientific production.

SOCIAL REPRESENTATIONS OF BASICS LIFE SUPPORT

Victims of cardiac arrest need immediate Basic Life Support, in order to preserve as much as possible, the flow of blood to the brain and heart and other vital organs, it is essential to gain time pending differentiated help, performing simple acts and practical (BLS) to save lives. Learn how to perform RPC is an interactive process that requires knowledge and skills, but at the same time an act of solidarity, social responsibility, civic consciousness, and a duty of citizenship. Because no one revives alone, it requires a coordinated work of a team, all citizens must join forces in a single goal: Save Lives, the massification of the BLS (RPC, 2014). We conducted an exploratory study that aimed to identify the social representations of basic life support in the general population. We used the technique of free association of words through a short questionnaire, we obtained a sample of 45 participants. The results show that participants were mostly female and 27 that fashion of age was in the age group 40 to 59 years. With regard to social representations, we find an organized structure follows the core: help, help to revive, and save is giving life, are in fact structural and consensual elements in basic life support. In more peripheral elements we find extremely important elements, which can be worked in a way so that the core is more efficient such as to act coordinately as a team in face of an accident, it can thus be successful in practice. The social representation of basic life support does not differ from that referred in the literature on the subject, but it is common knowledge that these skills can only be acquired if they are systematically trained, because they obey an algorithm that if it is not settled theoretical and instrumentally it is not effective in practice.

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations.

Embedded representations of social interactions

Social interactions have been the focus of social science research for a century, but their study has recently been revolutionized by novel data sources and by methods from computer science, network science, and complex systems science. The study of social interactions is crucial for understanding complex societal behaviours. Social interactions are naturally represented as networks, which have emerged as a unifying mathematical language to understand structural and dynamical aspects of socio-technical systems. Networks are, however, highly dimensional objects, especially when considering the scales of real-world systems and the need to model the temporal dimension. Hence the study of empirical data from social systems is challenging both from a conceptual and a computational standpoint. A possible approach to tackling such a challenge is to use dimensionality reduction techniques that represent network entities in a low-dimensional feature space, preserving some desired properties of the original data. Low-dimensional vector space representations, also known as network embeddings, have been extensively studied, also as a way to feed network data to machine learning algorithms. Network embeddings were initially developed for static networks and then extended to incorporate temporal network data. We focus on dimensionality reduction techniques for time-resolved social interaction data modelled as temporal networks. We introduce a novel embedding technique that models the temporal and structural similarities of events rather than nodes. Using empirical data on social interactions, we show that this representation captures information relevant for the study of dynamical processes unfolding over the network, such as epidemic spreading. We then turn to another large-scale dataset on social interactions: a popular Web-based crowdfunding platform. We show that tensor-based representations of the data and dimensionality reduction techniques such as tensor factorization allow us to uncover the structural and temporal aspects of the system and to relate them to geographic and temporal activity patterns.

Deploying and processing neural representations of signals

Neural representations (NR) have emerged in the last few years as a powerful tool to represent signals from several domains, such as images, 3D shapes, or audio. Indeed, deep neural networks have been shown capable of approximating continuous functions that describe a given signal with theoretical infinite resolution. This finding allows obtaining representations whose memory footprint is fixed and decoupled from the resolution at which the underlying signal can be sampled, something that is not possible with traditional discrete representations, e.g., grids of pixels for images or voxels for 3D shapes. During the last two years, many techniques have been proposed to improve the capability of NR to approximate high-frequency details and to make the optimization procedures required to obtain NR less demanding both in terms of time and data requirements, motivating many researchers to deploy NR as the main form of data representation for complex pipelines. Following this line of research, we first show that NR can approximate precisely Unsigned Distance Functions, providing an effective way to represent garments that feature open 3D surfaces and unknown topology. Then, we present a pipeline to obtain in a few minutes a compact Neural Twin® for a given object, by exploiting the recent advances in modeling neural radiance fields. Furthermore, we move a step in the direction of adopting NR as a standalone representation, by considering the possibility of performing downstream tasks by processing directly the NR weights. We first show that deep neural networks can be compressed into compact latent codes. Then, we show how this technique can be exploited to perform deep learning on implicit neural representations (INR) of 3D shapes, by only looking at the weights of the networks.

Learning representations for graph-structured socio-technical systems

The recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism. The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased emphasis on addressing the limitations of interpretability in graph representation learning. This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations.