A Feminist Affective Turn for Public Relations: Mothers, Passionate Publics, and the Childhood Vaccine Debate

This project proposes a feminist intervention in how affect and publics are theorized in public relations research. Drawing from extant literature, I argue that public relations theories of affect and publics have been apolitical and lack depth and context (Leitch & Motion, 2010a). Using the context of the online childhood vaccine debate, I illustrate several theories and concepts of the new feminist affective turn, as well as postmodern theories of affect, relevant to public relations research: (a) Public Feelings, “ugly” feelings, agency, and community (Cvetkovich, 2012; Ngai, 2007); (b) passionate politics (Mouffe, 2014); (c) postmodern assemblages, biopower, and body politics (Deleuze & Guattari, 1988; Foucault, 1984); (d) affective facts and logics of future threats (Massumi, 2010); and (e) affective ethics (Bertleson & Murphie, 2010). Scholarship in the areas of public relations, risk, feminist and postmodern affect theory, and the vaccine debate provided theoretical grounding for this project. My research questions asked: How is feminist affect theory embodied by mothers in the vaccine debate? How do mothers understand risks as affective facts in the vaccine debate (if at all)? What affective logics are used by mothers in the vaccine debate (if any)? And, What are sources of knowledge for mothers in the vaccine debate? Multi-sited online ethnographic methods were used to explore how feminist affect theory contributes to public relations research, including 29 one-on-one in-depth interviews with mothers of young children and participant observation of 15 online discussions about vaccines on parenting websites BabyCenter.com, TheBump.com, and WhatToExpect.com. I used snowball sampling to recruit interview participants and grounded theory (Glaser & Strauss, 1967) to analyze interview and online data. Results show that feminist affect theory contributes to theoretical and practical knowledge in public relations by politicizing and contextualizing understandings of publics and elucidating how affective facts and logics inform publics’ knowledge and choices, specifically in the context of risk. I also found evidence of suppression of dissent (Martin, 2015) and academic bias in vaccine debate research, which resulted in cultures of silence. Further areas of study included how specific contexts such as motherhood and issues of privilege and access affect publics’ experiences, knowledges, and choices.

STORYTELLING, EMOTION UNDERSTANDING, AND KINDERGARTNERS’ SOCIAL COMPETENCE

Interpreting others’ emotions is theoretically foundational for children’s social competence, yet little research contrasts Emotion Understanding (EU) types against their theoretical correlates. This study investigated kindergartners’ situationistic EU (attributing emotions based on external events) and mentalistic EU (attributing emotions from others’ mental states) in relation to Theory of Mind (ToM) and social skills, as rated by parents and teachers. The EU measures were expected to have low associations with one another and to relate differently to ToM and select social skills. Mentalistic EU was expected to be an important predictor of teacher-rated social skills. Results supported the hypothesis that mentalistic EU and situationistic EU are distinct constructs. However, both relate to ToM. Furthermore, while ToM and situationistic EU variables were included in the regression model, only vocabulary and mentalistic EU were significant predictors for teacher-rated social skills. Results indicate the importance of mentalistic EU in aspects of kindergartners’ social competence.

Probabilistic Models for Scalable Knowledge Graph Construction

In the past decade, systems that extract information from millions of Internet documents have become commonplace. Knowledge graphs -- structured knowledge bases that describe entities, their attributes and the relationships between them -- are a powerful tool for understanding and organizing this vast amount of information. However, a significant obstacle to knowledge graph construction is the unreliability of the extracted information, due to noise and ambiguity in the underlying data or errors made by the extraction system and the complexity of reasoning about the dependencies between these noisy extractions. My dissertation addresses these challenges by exploiting the interdependencies between facts to improve the quality of the knowledge graph in a scalable framework. I introduce a new approach called knowledge graph identification (KGI), which resolves the entities, attributes and relationships in the knowledge graph by incorporating uncertain extractions from multiple sources, entity co-references, and ontological constraints. I define a probability distribution over possible knowledge graphs and infer the most probable knowledge graph using a combination of probabilistic and logical reasoning. Such probabilistic models are frequently dismissed due to scalability concerns, but my implementation of KGI maintains tractable performance on large problems through the use of hinge-loss Markov random fields, which have a convex inference objective. This allows the inference of large knowledge graphs using 4M facts and 20M ground constraints in 2 hours. To further scale the solution, I develop a distributed approach to the KGI problem which runs in parallel across multiple machines, reducing inference time by 90%. Finally, I extend my model to the streaming setting, where a knowledge graph is continuously updated by incorporating newly extracted facts. I devise a general approach for approximately updating inference in convex probabilistic models, and quantify the approximation error by defining and bounding inference regret for online models. Together, my work retains the attractive features of probabilistic models while providing the scalability necessary for large-scale knowledge graph construction. These models have been applied on a number of real-world knowledge graph projects, including the NELL project at Carnegie Mellon and the Google Knowledge Graph.

THE RELATIVE SIGNIFICANCE OF SYNTACTIC KNOWLEDGE AND VOCABULARY KNOWLEDGE IN SECOND LANGUAGE LISTENING COMPREHENSION

The main purpose of the current study was to examine the role of vocabulary knowledge (VK) and syntactic knowledge (SK) in L2 listening comprehension, as well as their relative significance. Unlike previous studies, the current project employed assessment tasks to measure aural and proceduralized VK and SK. In terms of VK, to avoid under-representing the construct, measures of both breadth (VB) and depth (VD) were included. Additionally, the current study examined the role of VK and SK by accounting for individual differences in two important cognitive factors in L2 listening: metacognitive knowledge (MK) and working memory (WM). Also, to explore the role of VK and SK more fully, the current study accounted for the negative impact of anxiety on WM and L2 listening. The study was carried out in an English as a Foreign Language (EFL) context, and participants were 263 Iranian learners at a wide range of English proficiency from lower-intermediate to advanced. Participants took a battery of ten linguistic, cognitive and affective measures. Then, the collected data were subjected to several preliminary analyses, but structural equation modeling (SEM) was then used as the primary analysis method to answer the study research questions. Results of the preliminary analyses revealed that MK and WM were significant predictors of L2 listening ability; thus, they were kept in the main SEM analyses. The significant role of WM was only observed when the negative effect of anxiety on WM was accounted for. Preliminary analyses also showed that VB and VD were not distinct measures of VK. However, the results also showed that if VB and VD were considered separate, VD was a better predictor of L2 listening success. The main analyses of the current study revealed a significant role for both VK and SK in explaining success in L2 listening comprehension, which differs from findings from previous empirical studies. However, SEM analysis did not reveal a statistically significant difference in terms of the predictive power of the two linguistic factors. Descriptive results of the SEM analysis, along with results from regression analysis, indicated to a more significant role for VK.

Parsing, Generation and Grammar

Humans use their grammatical knowledge in more than one way. On one hand, they use it to understand what others say. On the other hand, they use it to say what they want to convey to others (or to themselves). In either case, they need to assemble the structure of sentences in a systematic fashion, in accordance with the grammar of their language. Despite the fact that the structures that comprehenders and speakers assemble are systematic in an identical fashion (i.e., obey the same grammatical constraints), the two ‘modes’ of assembling sentence structures might or might not be performed by the same cognitive mechanisms. Currently, the field of psycholinguistics implicitly adopts the position that they are supported by different cognitive mechanisms, as evident from the fact that most psycholinguistic models seek to explain either comprehension or production phenomena. The potential existence of two independent cognitive systems underlying linguistic performance doubles the problem of linking the theory of linguistic knowledge and the theory of linguistic performance, making the integration of linguistics and psycholinguistic harder. This thesis thus aims to unify the structure building system in comprehension, i.e., parser, and the structure building system in production, i.e., generator, into one, so that the linking theory between knowledge and performance can also be unified into one. I will discuss and unify both existing and new data pertaining to how structures are assembled in understanding and speaking, and attempt to show that the unification between parsing and generation is at least a plausible research enterprise. In Chapter 1, I will discuss the previous and current views on how parsing and generation are related to each other. I will outline the challenges for the current view that the parser and the generator are the same cognitive mechanism. This single system view is discussed and evaluated in the rest of the chapters. In Chapter 2, I will present new experimental evidence suggesting that the grain size of the pre-compiled structural units (henceforth simply structural units) is rather small, contrary to some models of sentence production. In particular, I will show that the internal structure of the verb phrase in a ditransitive sentence (e.g., The chef is donating the book to the monk) is not specified at the onset of speech, but is specified before the first internal argument (the book) needs to be uttered. I will also show that this timing of structural processes with respect to the verb phrase structure is earlier than the lexical processes of verb internal arguments. These two results in concert show that the size of structure building units in sentence production is rather small, contrary to some models of sentence production, yet structural processes still precede lexical processes. I argue that this view of generation resembles the widely accepted model of parsing that utilizes both top-down and bottom-up structure building procedures. In Chapter 3, I will present new experimental evidence suggesting that the structural representation strongly constrains the subsequent lexical processes. In particular, I will show that conceptually similar lexical items interfere with each other only when they share the same syntactic category in sentence production. The mechanism that I call syntactic gating, will be proposed, and this mechanism characterizes how the structural and lexical processes interact in generation. I will present two Event Related Potential (ERP) experiments that show that the lexical retrieval in (predictive) comprehension is also constrained by syntactic categories. I will argue that the syntactic gating mechanism is operative both in parsing and generation, and that the interaction between structural and lexical processes in both parsing and generation can be characterized in the same fashion. In Chapter 4, I will present a series of experiments examining the timing at which verbs’ lexical representations are planned in sentence production. It will be shown that verbs are planned before the articulation of their internal arguments, regardless of the target language (Japanese or English) and regardless of the sentence type (active object-initial sentence in Japanese, passive sentences in English, and unaccusative sentences in English). I will discuss how this result sheds light on the notion of incrementality in generation. In Chapter 5, I will synthesize the experimental findings presented in this thesis and in previous research to address the challenges to the single system view I outlined in Chapter 1. I will then conclude by presenting a preliminary single system model that can potentially capture both the key sentence comprehension and sentence production data without assuming distinct mechanisms for each.