University at Albany, State University of New York
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“Know Your Facts Before Taking a Stand:” The Schenectady County League of Women Voter’s Impact on Local Policy through Civic Education, 1919-1945
Get PDFThis thesis explores the pivotal role of the Schenectady County League of Women Voters in shaping local policy and politics through civic education during the interwar years. Empowered by the principle “know your facts before taking a stand and going public,” the Schenectady County League educated both its members and the public on policy issues in a nonpartisan and all partisan way. The Schenectady County League’s strategic emphasis on nonpartisan civic education empowered its members to become well-informed advocates for policy change. By prioritizing issue-based stances over partisan politics, the Schenectady County League expanded its influence beyond traditional boundaries and addressed crucial challenges in the community. Through initiatives such as study groups, monthly bulletins, and radio broadcasts, the Schenectady County League effectively educated the public on local and state affairs, fostering a culture of informed civic participation. After the local branch’s formation in 1925, it immediately worked on reform in areas of child welfare at the local, state, and national level. When the Great Depression hit in 1929, the Schenectady County League shifted focus towards government efficiency and education, emphasizing the need for crucial reform in both areas. After 1936, the Schenectady County League began to focus on international cooperation until the national war effort took root in its agenda in 1941. The Schenectady County League then worked to bridge the gap between national emergencies and local realities, empowering the public by using civic education to make their voices heard
Best Practices for 1st Grade Mathematics Education
Get PDFThis paper explores the importance of early mathematics instruction and best-practices for first-grade mathematics education according to research. Moreover, it explores how first-grade educators can incorporate effective evidence-based practices to maximize student learning and enhance mathematical development. Research suggests that prioritizing early mathematics instruction is pivotal in building foundational mathematical skills that affect subsequent learning, and establishing students\u27 attitudes towards the subject and their beliefs about their mathematical capabilities. Highly effective practices to ensure students receive the adequate early instruction they deserve include building students\u27 mathematical vocabulary knowledge so that students can successfully engage in mathematical conversations, implementing Child-centered learning opportunities, and providing students with or at-risk of mathematics difficulty with explicit instruction. I use my observations to consider how technology and teachers can set students up for long-term success and provide students positive and effective early mathematical learning experiences
Upstream Local Actor and Community-Based Potential in Mass Atrocity Prevention
Get PDFThe concept of genocide and mass atrocity prevention is still relatively new. Research on genocide prevention would not begin until post 1995 following the genocides in Rwanda and the former Yugoslavia (Rosenberg & Zucker, 2015). Therefore, there is much left to be uncovered in the field (Rosenberg & Zucker, 2015), despite that genocides and mass atrocities have occurred for centuries prior to the coining of the term and continue to occur to this day (Bellamy, 2015).
The term genocide falls under atrocity crimes, an umbrella term that refers to genocide, crimes against humanity, war crimes, and occasionally ethnic cleansing (United Nations, 2014). It is worth considering that mass atrocities are complex and fluid social processes (Rosenberg, 2012) that can heighten according to the United Nations (1948). The legal definition requires that the perpetrators of a genocide have the intent to destroy a “national, ethnical, racial, or religious group” either in part or in totality by killing, inflicting conditions aimed to physically destroy, transferring children from, causing bodily or mental harm to, or imposing measures to prevent births of a target group. Notably, some historically targeted demographics are not included and there is a strong emphasis on the phrase “with intent” in the definition (Hinton, 2023). Due to the mentioned groups listed, the complicated lead-up to genocide, and the emphasis on proving the intent of the perpetrators, genocide is difficult to charge one with. This challenge, the limited categories of target groups in the legal definition of genocide, and the overlapping nature of atrocity crimes make way for “mass atrocity” as a popular term to describe the field. Deriving from atrocity crimes, mass atrocity is an act with no legal definition but is characterized as
“large-scale systematic violence against civilian populations” as stated by Scott Strauss (2016).
Well acknowledged in the field is that all societies are at risk of mass atrocity (Waller, 2017). Therefore, although the circumstances of each atrocity are unique (Bellamy, 2015), there is a widely used general framework of analysis to judge the risk of an atrocity crime being committed by any State put forth by the United Nations (UN). This Framework of Analysis for Atrocity Crimes recognizes eight risk factors common to all types of atrocity crimes and then cites two more risk factors for each individual type (United Nations, 2014). The model is intended to be used by large-scale actors, as the United Nations (2014) describes atrocity prevention to be “primarily the responsibility of individual States” as well as members of the international community and regional State alliances. More specifically, this refers to States that have adopted the UN’s 2005 Responsibility to Protect document, meaning they are compelled to assist in the de-escalation or prevention of atrocity crimes as they arise (United Nations, n.d.). As of 2007 the International Court of Justice has also considered genocide prevention a legal obligation upon States (Rosenberg, 2012), furthering a State’s obligation to evaluate the risk of atrocity and prevent it as needed. Important to note, the risk factors in this framework require dangerously worsening civilian safety conditions to prove the risk of atrocity being enacted and these risk factors largely describe situations in which civilians are in danger of being directly killed (United Nations, 2014)
Effects of Dehumanization and Disgust-Eliciting Language on Attitudes Toward Immigration: A Sentiment Analysis of Twitter Data
Get PDFAttitudes towards immigration have been shown to be driven by dehumanization and disgust. The more people dehumanize immigrants and the more disgusted they feel, the more negative attitudes they tend to have toward immigrants. However, little is known about how exposure to social media content that links dehumanization, disgust, and immigration influences users’ attitudes on this issue. This is important to consider because the majority of adults in the United States are on social media. We used Twitter data, machine learning, and sentiment analysis to investigate whether exposure to dehumanizing or disgust-eliciting tweets about immigration impacts users’ own sentiment toward immigration over time. Our results were in some ways consistent and in other ways inconsistent with prior literature. They showed that either dehumanizing or disgust-eliciting language appears in 66% of our sample of tweets pertaining to immigration. Unexpectedly, however, exposure to both kinds of language in tweets about immigration related to small increases in positive sentiment about immigration over time. There was evidence of Granger-causality only for dehumanizing language, however, and only when controlling for the political affiliation of the communicator. These findings indicate that social media exposure may influence public perceptions of immigrants and immigration issues in unexpected ways
Testing an Integrated Dual Diathesis-Threat Model of Authoritarianism
Get PDFThe Dual Diathesis-Threat Model of Authoritarianism proposes parallel motivational pathways by which similar personality features could predispose liberals and conservatives to different forms of authoritarianism. Specifically, the model posits that cognitive rigidity serves as a shared dispositional ‘diathesis’ of right- and left-wing authoritarianism ‘activated’ by distinct threats. For cognitively rigid conservatives, threats of change (i.e., threats to cohesion/conventions, cultural shifts) were expected to enhance the embrace of RWA; while for cognitively rigid liberals, threats to change (i.e., oppression, inequities, barriers to social progress) were predicted to promote LWA. Two studies examined this premise. In Study One, American citizens (N=256) of different political backgrounds reported qualitatively distinct threats and differently ranked a series of threats. Thematic content analysis revealed that for liberals (n=124), concerns about climate change, MAGA, inequality, poverty, and prejudice were preeminent, whereas for conservatives (n=70), issues like immigration, crime, war, terrorism, and perceived cultural decline were front of mind. Conservatives and liberals consistently, but not exclusively, framed their responses in terms of threats of and to change. Moderates (n=62) were particularly concerned about social and political division; overall, however, moderates’ responses and threat rankings paralleled conservatives’. Respondents were uniformly threatened by economic issues and their political opponents, the latter of whom were the top-ranked threats across ideological groups (for liberals, MAGA; for conservatives and moderates, the Woke Mob). Study Two tested the DDT model in a combined sample (N=465) of American undergraduates (n=206) and laypeople (n=259) using an experimental manipulation of threat of and to change. Hypotheses were partially supported, but there was little support for the DDT framework as a whole. Cognitive rigidity and threats of and to change predicted greater endorsement of authoritarian responses. However, these effects were not particular to conservatives or liberals. Moreover, a two-way interaction between cognitive rigidity and political ideology suggested that high cognitive rigidity enhanced authoritarianism among liberals, but not conservatives. Implications for the theoretical landscape are discussed, and directions proposed for future research
Sparse Representation Learning for Temporal Networks
Get PDFTemporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data (i.e., network or temporal
priors but not both). To address this shortcoming of existing work I propose
novel frameworks for representation learning of temporal networks via appropriate dictionary bases. The methods which comprise this thesis utilize and incorporate network and temporal priors to learn novel representations in two keyways: data aggregation and multidictionary representation. The learned representations achieve state-of-the-art performance on a wide variety of tasks such as missing value imputation, future value forecasting, community detection, periodicity analysis, change point and anomaly detection, node classification, and link prediction. Beyond their quantitative performance, my proposed methodologies reveal insights into the underlying graph-time behavioral patterns in datasets from diverse domains. I demonstrate this through various case studies including discovery of communities
in global air traffic, identification of meaningful changes in the activity of users on the social network of reddit, and extraction of common interest groups among streamers on the Twitch platform
Regulator Preferences and Underinvestment in Drinking Water Infrastructure
Get PDFThe infrastructure of U.S. drinking water systems faces chronic issues of low quality and underinvestment. As the water utilities in the U.S. are typically managed and regulated at the local municipal level, investment levels depend on the municipal regulator\u27s preferences. Drawing from a dynamic framework, I specify and estimate the local regulators\u27 objective function over drinking water infrastructure investment. This framework highlights the tradeoff between profit and consumer surplus and reveals how regulator preferences can deviate from net surplus optimization, resulting in underinvestment. My findings provide significant evidence that the drinking water infrastructure is underinvested. I reveal that the regulator\u27s higher preference weight on utility profit over consumer surplus leads to a social welfare loss of 38 thousand dollars per utility per year. My counterfactual analyses proposes two policy strategies to fix this loss, including subsidies for water utility and technological advancements
Workshop: Grant Writing
No full textThe goal of this workshop is to highlight best practices in grant writing. In this workshop Dr. Brandon Behlenforf will go over the do\u27s and don\u27ts of of grant writing
