The Issue with Issues Management: An Engagement Approach to Integrate Gender and Emotion into Issues Management

Using sexual assault on college campuses as a context for interrogating issues management, this study offers a normative model for inclusive issues management through an engagement approach that can better account for the gendered and emotional dimensions of issues. Because public relations literature and research have offered little theoretical or practical guidance for how issues managers can most effectively deal with issues such as sexual assault, this study represents a promising step forward. Results for this study were obtained through 32 in-depth interviews with university issues managers, six focus groups with student populations, and approximately 92 hours of participant observation. By focusing on inclusion, this revised model works to have utility for an array of issues that have previously fallen outside of the dominant masculine and rationale spheres that have worked to silence marginalized publics’ experiences. Through adapting previous issues management models to focus on inclusion at the heart of a strategic process, and engagement as the strategy for achieving this, this study offers a framework for ensuring more voices are heard—which enables organizations to more effectively communicate with their publics. Additionally, findings from this research may also help practitioners at different types of organizations develop better, and proactive, communication strategies for handling emotional and gendered issues as to avoid negative media attention and work to change organizational culture.

Relationship between subjective wellbeing and weight perception in children and adolescents: Gender and age differences

The relation between weight status (Body Mass Index - BMI), weight perception and subjective wellbeing remains unclear. Several studies conclude that discrepancies can be found between weight status and weight perception, among children and adolescents. The present study aims at investigating the associations between subjective wellbeing and individual characteristics, among children and adolescents. The sample included 1200 children and adolescents (51.7 % girls, aged 9 to 17). Their mean age was 12.55 years (SD = 1.61). The questionnaire was completed in school context, asking about the subjective wellbeing, use of self-regulation, eating behavior awareness/care, weight perception and sociodemographic questions such as age, gender and BMI. The study found a strong association between BMI and weight perception, although subjective wellbeing was better explained by weight perception than by BMI. Eating awareness and self-regulation also played an important role in subjective controlling for age and gender. Age and gender interfere in the relation between subjective wellbeing and other variables. The multiple regression model is more robust and explicative for girls and older children. Psychological factors related to weight, such as weight perception, self-regulation and eating awareness have a stronger explicative impact in subjective wellbeing compared to physical aspects, such as Body Mass Index. The relation between subjective wellbeing and weight is influence by age and gender.

Search for Higgs Boson Production Beyond the Standard Model Using the Razor Kinematic Variables in pp Collisions at √s=8 TeV and Optimization of Higgs Boson Identification Using a Quantum Annealer

In the first part of this thesis we search for beyond the Standard Model physics through the search for anomalous production of the Higgs boson using the razor kinematic variables. We search for anomalous Higgs boson production using proton-proton collisions at center of mass energy √s=8 TeV collected by the Compact Muon Solenoid experiment at the Large Hadron Collider corresponding to an integrated luminosity of 19.8 fb^-1.

In the second part we present a novel method for using a quantum annealer to train a classifier to recognize events containing a Higgs boson decaying to two photons. We train that classifier using simulated proton-proton collisions at √s=8 TeV producing either a Standard Model Higgs boson decaying to two photons or a non-resonant Standard Model process that produces a two photon final state.

The production mechanisms of the Higgs boson are precisely predicted by the Standard Model based on its association with the mechanism of electroweak symmetry breaking. We measure the yield of Higgs bosons decaying to two photons in kinematic regions predicted to have very little contribution from a Standard Model Higgs boson and search for an excess of events, which would be evidence of either non-standard production or non-standard properties of the Higgs boson. We divide the events into disjoint categories based on kinematic properties and the presence of additional b-quarks produced in the collisions. In each of these disjoint categories, we use the razor kinematic variables to characterize events with topological configurations incompatible with typical configurations found from standard model production of the Higgs boson.

We observe an excess of events with di-photon invariant mass compatible with the Higgs boson mass and localized in a small region of the razor plane. We observe 5 events with a predicted background of 0.54 ± 0.28, which observation has a p-value of 10^-3 and a local significance of 3.35σ. This background prediction comes from 0.48 predicted non-resonant background events and 0.07 predicted SM higgs boson events. We proceed to investigate the properties of this excess, finding that it provides a very compelling peak in the di-photon invariant mass distribution and is physically separated in the razor plane from predicted background. Using another method of measuring the background and significance of the excess, we find a 2.5σ deviation from the Standard Model hypothesis over a broader range of the razor plane.

In the second part of the thesis we transform the problem of training a classifier to distinguish events with a Higgs boson decaying to two photons from events with other sources of photon pairs into the Hamiltonian of a spin system, the ground state of which is the best classifier. We then use a quantum annealer to find the ground state of this Hamiltonian and train the classifier. We find that we are able to do this successfully in less than 400 annealing runs for a problem of median difficulty at the largest problem size considered. The networks trained in this manner exhibit good classification performance, competitive with the more complicated machine learning techniques, and are highly resistant to overtraining. We also find that the nature of the training gives access to additional solutions that can be used to improve the classification performance by up to 1.2% in some regions.

Gender, Behavioral Assessment of Negative Reinforcement Driven Risk Taking Propensity, and Cigarette Smoking

Cigarette smoking remains the leading preventable cause of death and disability in the United States and most often is initiated during adolescence. An emerging body of research suggests that a negative reinforcement model may explain factors that contribute to tobacco use during adolescence and that negative reinforcement processes may contribute to tobacco use to a greater extent among female adolescents than among male adolescents. However, the extant literature both on the relationship between negative reinforcement processes and adolescent tobacco use as well as on the relationship between gender, negative reinforcement processes, and adolescent tobacco use is limited by the sole reliance on self-report measures of negative reinforcement processes that may contribute to cigarette smoking. The current study aimed to further disentangle the relationships between negative reinforcement based risk taking, gender and tobacco use during older adolescence by utilizing a behavioral analogue measure of negative reinforcement based risk taking, the Maryland Resource for the Behavioral Utilization of the Reinforcement of Negative Stimuli (MRBURNS). Specifically, we examined the relationship between pumps on the MRBURNS, an indicator of risk taking, and smoking status as well as the interaction between MRBURNS pumps and gender for predicting smoking status. Participants included 103 older adolescents (n=51 smokers, 50.5% female, Age (M(SD) = 19.41(1.06)) who all attended one experimental session during which they completed the MRBURNS as well as self-report measures of tobacco use, nicotine dependence, alcohol use, depression, and anxiety. We utilized binary logistic regressions to examine the relationship between MRBURNS pumps and smoking status as well as the interactive effect of MRBURNS pumps and gender for predicting smoking status. Controlling for relevant covariates, pumps on the MRBURNS did not significantly predict smoking status and the interaction between pumps on the MRBURNS and gender also did not significantly predict smoking status. These findings highlight the importance of future research examining various task modifications to the MRBURNS as well as the need for replications of this study with larger, more diverse samples.

The impact of gender differences on consumer-brand relationships

Dissertação de Mestrado, Marketing, Faculdade de Economia, Universidade do Algarve, 2016

Changing family models in Spain: the impact of the sociodemographic factors

Abstract During the last few decades, there has been an increasing international recognition of the studies related to the analysis of the family models change, the focus being the determinants of the female employment and the problems related to the work family balance (Lewis, 2001; Petit & Hook, 2005Saraceno, Crompton & Lyonette, 20062008; Pfau-Effinger, 2012). The majority of these studies have been focused on the analysis of the work-family balance problems as well as the effectiveness of the family and gender policies in order to encourage female employment (Korpi et al., 2013). In Spain, special attention has been given to the family policies implemented, the employability of women and on the role of the father in the family (Flaquer et al., 2015; Meil, 2015); however, there has been far less emphasis on the analysis of the family cultural models (González and Jurado, 2012; Crespi and Moreno, 2016). The purpose of this paper is to present some of the first results on the influence of the socio-demographic factors on the expectations and attitudes about the family models. This study offers an analytical reflection upon the foundation of the determinants of the family ambivalence in Spain from the cultural and the institutional dimension. This study shows the Spanish family models of preferences following the Pfau-Effinger (2004) classification of the famiy living arrangements. The reason for this study is twofold; on the one hand, there is confirmed the scarcity of studies that have focused their attention on this objective in Spain; on the other hand, the studies carried out in the international context have confirmed the analytical effectiveness of researching on the attitude and value changes to explain the meaning and trends of the family changes. There is also presented some preliminary results that have been obtained from the multinomial analysis related to the influence of the socio-demographic factors on the family model chosen by the individuals in Spain (father and mother working full time; mother part-time father full-time; mother not at work father full-time; mother and father part-time). 3 The database used has been the International Social Survey Programme: Family and Changing Gender Roles IV- ISSP 2012-. Spain is the only country of South Europe that has participated in the survey. For this reason it has been considered as a representative case study.

Application of a Bivariate Probit Model to Investigate the Intended Evacuation from Hurricane

With evidence of increasing hurricane risks in Georgia Coastal Area (GCA) and Virginia in the U.S. Southeast and elsewhere, understanding intended evacuation behavior is becoming more and more important for community planners. My research investigates intended evacuation behavior due to hurricane risks, a behavioral survey of the six counties in GCA under the direction of two social scientists with extensive experience in survey research related to citizen and household response to emergencies and disasters. Respondents gave answers whether they would evacuate under both voluntary and mandatory evacuation orders. Bivariate probit models are used to investigate the subjective belief structure of whether or not the respondents are concerned about the hurricane, and the intended probability of evacuating as a function of risk perception, and a lot of demographic and socioeconomic variables (e.g., gender, military, age, length of residence, owning vehicles).

The Influence of Gender on Perceived Treatment Need among a Community Sample of Substance Users

Purpose: Most individuals do not perceive a need for substance use treatment despite meeting diagnostic criteria for substance use disorders and they are least likely to pursue treatment voluntarily. There are also those who perceive a need for treatment and yet do not pursue it. This study aimed to understand which factors increase the likelihood of perceiving a need for treatment for individuals who meet diagnostic criteria for substance use disorders in the hopes to better assist with more targeted efforts for gender-specific treatment recruitment and retention. Using Andersen and Newman’s (1973/2005) model of individual determinants of healthcare utilization, the central hypothesis of the study was that gender moderates the relationship between substance use problem severity and perceived treatment need, so that women with increasing problems due to their use of substances are more likely than men to perceive a need for treatment. Additional predisposing and enabling factors from Andersen and Newman’s (1973/2005) model were included in the study to understand their impact on perceived need. Method: The study was a secondary data analysis of the 2010 National Survey on Drug Use and Health (NSDUH) using logistic regression. The weighted sample consisted of a total 20,077,235 American household residents (The unweighted sample was 5,484 participants). Results of the logistic regression were verified using Relogit software for rare events logistic regression due to the rare event of perceived treatment need (King & Zeng, 2001a; 2001b). Results: The moderating effect of female gender was not found. Conversely, men were significantly more likely than women to perceive a need for treatment as substance use problem severity increased. The study also found that a number of factors such as race, ethnicity, socioeconomic status, age, marital status, education, co-occurring mental health disorders, and prior treatment history differently impacted the likelihood of perceiving a need for treatment among men and women. Conclusion: Perceived treatment need among individuals who meet criteria for substance use disorders is rare, but identifying factors associated with an increased likelihood of perceiving need for treatment can help the development of gender-appropriate outreach and recruitment for social work treatment, and public health messages.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

Do gender and year of study affect the ability of the theory of planned behaviour to predict binge-drinking intentions and episodes?

Background: The present study tested the utility of the theory of planned behaviour (TPB), augmented with anticipated regret, as a model to predict binge-drinking intentions and episodes among female and male undergraduates and undergraduates in different years of study. Method: Undergraduate students (N = 180, 54 males, 126 females, 60 per year of study) completed baseline measures of demographic variables, binge-drinking episodes (BDE), TPB constructs and anticipated regret. BDE were assessed one-week later. Results: The TPB accounted for 60% of the variance in female undergraduates' intentions and 54% of the variance in male undergraduates' intentions. The TPB accounted for 57% of the variance in intentions in first-year undergraduates, 63% of the variance in intentions in second-year undergraduates and 68% of the variance in intentions in final-year undergraduates. Follow-up BDE was predicted by intentions and baseline BDE for female undergraduates as well as second- and final-year undergraduates. Baseline BDE predicted male undergraduates’ follow-up BDE and first-year undergraduates’ follow-up BDE. Conclusion: Results show that while the TPB constructs predict undergraduates’ binge-drinking intentions, intentions only predict BDE in female undergraduates, second- and final-year undergraduates. Implications of these findings for interventions to reduce binge drinking are outlined.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

Good is generally more alike than bad in the information ecology: Investigating the case of (the ABC model of) group stereotypes

On most if not all evaluatively relevant dimensions such as the temperature level, taste intensity, and nutritional value of a meal, one range of adequate, positive states is framed by two ranges of inadequate, negative states, namely too much and too little. This distribution of positive and negative states in the information ecology results in a higher similarity of positive objects, people, and events to other positive stimuli as compared to the similarity of negative stimuli to other negative stimuli. In other words, there are fewer ways in which an object, a person, or an event can be positive as compared to negative. Oftentimes, there is only one way in which a stimulus can be positive (e.g., a good meal has to have an adequate temperature level, taste intensity, and nutritional value). In contrast, there are many different ways in which a stimulus can be negative (e.g., a bad meal can be too hot or too cold, too spicy or too bland, or too fat or too lean). This higher similarity of positive as compared to negative stimuli is important, as similarity greatly impacts speed and accuracy on virtually all levels of information processing, including attention, classification, categorization, judgment and decision making, and recognition and recall memory. Thus, if the difference in similarity between positive and negative stimuli is a general phenomenon, it predicts and may explain a variety of valence asymmetries in cognitive processing (e.g., positive as compared to negative stimuli are processed faster but less accurately). In my dissertation, I show that the similarity asymmetry is indeed a general phenomenon that is observed in thousands of words and pictures. Further, I show that the similarity asymmetry applies to social groups. Groups stereotyped as average on the two dimensions agency / socio-economic success (A) and conservative-progressive beliefs (B) are stereotyped as positive or high on communion (C), while groups stereotyped as extreme on A and B (e.g., managers, homeless people, punks, and religious people) are stereotyped as negative or low on C. As average groups are more similar to one another than extreme groups, according to this ABC model of group stereotypes, positive groups are mentally represented as more similar to one another than negative groups. Finally, I discuss implications of the ABC model of group stereotypes, pointing to avenues for future research on how stereotype content shapes social perception, cognition, and behavior.

The Strange Case of Eugenics: A Subject’s Ontogeny in a Long-Lived Classification Scheme and the Question of Collocative Integrity

Classification schemes are built at a particular point in time; at inception, they reflect a worldview indicative of that time. This is their strength, but results in potential weak- nesses as worldviews change. For example, if a scheme of mathematics is not updated even though the state of the art has changed, then it is not a very useful scheme to users for the purposes of information retrieval. However, change in schemes is a good thing. Changing allows designers of schemes to update their model and serves as a responsible mediator between resources and users. But change does come at a cost. In the print world, we revise universal clas- sification schemes—sometimes in drastic ways—and this means that over time, the power of a classification scheme to collocate is compromised if we do not account for scheme change in the organization of affected physical resources. If we understand this phenomenon in the print world, we can design ameliorations for the digital world.

Layered Information Modeling and a Knowledge Organization Paradigm: Inherent Classification and the Design of Interoperable Systems

In knowledge technology work, as expressed by the scope of this conference, there are a number of communities, each uncovering new methods, theories, and practices. The Library and Information Science (LIS) community is one such community. This community, through tradition and innovation, theories and practice, organizes knowledge and develops knowledge technologies formed by iterative research hewn to the values of equal access and discovery for all. The Information Modeling community is another contributor to knowledge technologies. It concerns itself with the construction of symbolic models that capture the meaning of information and organize it in ways that are computer-based, but human understandable. A recent paper that examines certain assumptions in information modeling builds a bridge between these two communities, offering a forum for a discussion on common aims from a common perspective. In a June 2000 article, Parsons and Wand separate classes from instances in information modeling in order to free instances from what they call the “tyranny” of classes. They attribute a number of problems in information modeling to inherent classification – or the disregard for the fact that instances can be conceptualized independent of any class assignment. By faceting instances from classes, Parsons and Wand strike a sonorous chord with classification theory as understood in LIS. In the practice community and in the publications of LIS, faceted classification has shifted the paradigm of knowledge organization theory in the twentieth century. Here, with the proposal of inherent classification and the resulting layered information modeling, a clear line joins both the LIS classification theory community and the information modeling community. Both communities have their eyes turned toward networked resource discovery, and with this conceptual conjunction a new paradigmatic conversation can take place. Parsons and Wand propose that the layered information model can facilitate schema integration, schema evolution, and interoperability. These three spheres in information modeling have their own connotation, but are not distant from the aims of classification research in LIS. In this new conceptual conjunction, established by Parsons and Ward, information modeling through the layered information model, can expand the horizons of classification theory beyond LIS, promoting a cross-fertilization of ideas on the interoperability of subject access tools like classification schemes, thesauri, taxonomies, and ontologies. This paper examines the common ground between the layered information model and faceted classification, establishing a vocabulary and outlining some common principles. It then turns to the issue of schema and the horizons of conventional classification and the differences between Information Modeling and Library and Information Science. Finally, a framework is proposed that deploys an interpretation of the layered information modeling approach in a knowledge technologies context. In order to design subject access systems that will integrate, evolve and interoperate in a networked environment, knowledge organization specialists must consider a semantic class independence like Parsons and Wand propose for information modeling.