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Since its emergence in late December 2010, the Arab Spring narrative has sparked many controversies among researchers, commentators, analysts, and scholars from different disciplines around the world in terms of the causes and the reasons behind it and even its name: ‘Arab Spring’. This study explores the Arab Spring narrative from its emergence to its continuing dénouement in the English and Arabic mainstream news media from corpus-linguistic and critical discourse analytic perspectives. The Arab Spring bilingual corpus consists of two main sub-corpora, English and Arabic, compiled from LexisNexis and other news websites. Totalling 15,088 articles and 11,522,846 words, the English sub-corpus consists of 7,018 texts with total of 5,901,416 words, while the Arabic sub-corpus comprises 8,070 news texts and a total of 5,621,430 words. Taken from prominent news media outlets from Western, Arab and Islamic countries, and divided into two major text types (news and editorials and opinions) with date range coverage from 15 June 2010 until 31 August 2013, it allows us to diachronically and synchronically examine the discursive construction of the Arab Spring narrative. Combining quantitative and qualitative methods associated with Corpus Linguistics (CL) and Critical Discourse Analysis (CDA), the current study explores the key topics associated with the Arab Spring at both the linguistic as well as the semantic levels by means of frequency, keyword (KKW list function in my case), collocation list functions and concordance. Analysis also identifies the main news actors and news values. Actors and events are represented, negatively and positively by means of lexical choice, and the different presentation strategies indicate that many of the Arab Spring news stories are politically, socially, and ideologically polarized. The contrasting themes/concepts within the resulting semantic categories (by means of pairs of items with positive/negative connotations) are also prevalent. For example, at the lexical level the following contrasting pairs are revealed: democracy/dictatorship; religious, sectarian/secular; peace/ violence; government/regime; allies/enemy; corruption/ reform, opposition/ support. Similarly, at the grammatical level items, such as pro/anti, is/non- and not, also indicate the contrastive as well as the polarizing nature of the Arab Spring narrative.

Abstract: In recent years, mazes have been used to study robot behavior by assessing their ability to navigate a maze using various methods. The act of finding a path through a maze from beginning to end is known as maze solving. Some maze-solving methods are employed for use by an agent with no previous knowledge of the maze, while others are designed for use by someone or a computer program capable of seeing the whole maze at once. We consider a situation in which multiple agents are randomly distributed inside an arbitrary rectangular maze and have no previous knowledge of the maze. We provide a solution in the form of an algorithm for the agents to cooperate collaboratively to discover and achieve the hidden goal. We divide our algorithm into two major phases and present rules for each, with each agent is programmed to follow these rules individually. We explained the algorithm's implementation by addressing the challenges we have such that the agents can follow the algorithm in such a way that all agents may move simultaneously. We evaluate our approach using a computer simulation of a square-shaped maze with varying sizes and a variable number of agents. The algorithm performs well in the simulation, is efficient, and reflects the trade-off between utilizing a single agent and multiple agents. We provided the solution's results for both phases. We then validate our algorithm on a physical system consisting of a real maze and many robots. Our solution is primarily based on cooperating and working parallel with all agents.

This dissertation assesses a new measure of gender. In doing so, it addresses a glaring inconsistency between how sociologists conceptualize gender and how we measure it (i.e., with sex categories) in most quantitative sociological literature. Research that only measures sex effectively hides the variability in individual social identities that is related to sociological outcomes of interest. Unfortunately, most existing quantitative gender scales are impractical for use in large scale data collection. In this work, I investigated the Gender Self-perception Scale (GSPS) as an alternative survey measure of gender using three projects. For the first project (chapter 2), I used the GSPS and sex measures to predict warmth and competence across two data sources Strong associations between sex and gender were found for both groups, but less so for competence than warmth, and gender (as measured by the GSPS) was not fully explained by sex. In the second project (chapter 3), I examined how people define gender, gender identity, and masculinity and femininity, and how they think about two versions of the GSPS using cognitive interviews with 13 cisgender and 7 trans respondents. Results indicated similar understandings of gender by gender status, and an overall positive response to the GSPS. The GSPS appears to capture the degree to which the respondent fulfills social stereotypes, indicating that it may be most beneficial for interactional research. In the final project (chapter 4), I examined the relationship between sex, gender, and work-related outcomes within a high gender minority sample of employed respondents. Neither sex nor the GSPS were consistent predictors across the outcomes of interest, which may indicate a reduction in the impact of these characteristics within the workplace, at least for those privileged in terms of education and occupation as this sample was. Overall, these results point to changing gender norms and expectations, and variability in experiences that are lost when we reduce our analyses to only sex. More research is needed using the GSPS, but it is an option for researchers going forward who would like to measure gender in a parsimonious way. Advisor: Jolene Smyth

In a highly competitive labor force, human capital is a marketable resource. However, the human capital model fails to explain the substantial number of adults pursuing education after they enter the workforce. Not only are increasing numbers of adult students pursuing credentials in the form of degrees and certificates, they pursue other types of education as well. Using the 2005 National Household Education Survey on adult education, I predict participation patterns in workers over the age of 25 using queuing and intersectionality theories to explain gender, race and age variations. For adults pursuing education, employer support demonstrates racial/ethnic differences across employees. Latino employees receive less support for education after controlling for human capital and queuing effects. Overall, employees with more education credentials benefit most from employer support.