Readings Flashcards
(7 cards)
week 1: GHOST-HOUSE BUSTERS: THE ELECTORAL RESPONSE TO A LARGE ANTI–TAX EVASION PROGRAM*
By Lorenzo Casaburi and Ugo Troiano
- The study focuses on the electoral impact of Italy’s Ghost Buildings program, an anti-tax evasion initiative.
- It found that a higher intensity of the program at the local level increased the reelection chances of incumbent mayors.
- This positive electoral effect was stronger in areas with lower tolerance for tax evasion.
- The effect was also more pronounced where local governments were more efficient in providing public goods.
week 3: Numbers and Newness: The Descriptive and Substantive Representation of Women
By Karen Beckwith
- The article critiques number-based theories like “critical mass” and “sex-ratio theory” that attempt to link the number of women in politics to the representation of women’s interests.
- It argues that these theories are often under-theorized and face methodological challenges.
- The article proposes focusing on “newness,” defined as a substantial increase in the number of newly elected women, as a potentially more insightful factor influencing women’s substantive representation.
- It suggests that the intersection of “numbers” and “newness” creates uncertainties and dynamics that can affect legislative behavior and policy outcomes.
Week 3: Judgment and Measurement in Political Science
By Andreas Schedler
and critical component of political measurement**, rather than something to be eliminated.
* It critiques the ideal of “bureaucratic measurement” that relies solely on rules and observations, stating it is neither feasible nor desirable.
* The author contends that in political science, especially when measuring complex concepts, expert judgment is often essential due to unobservable realities, incomplete information, and conceptual complexity.
* The article emphasizes the need to rationalize and discipline the use of judgment in measurement through clear standards for expert selection, comparability, transparency, divergence reconciliation, and accountability.
We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together” by Justin Grimmer
Main Findings: This article argues that analyzing big data to address societal problems requires a social science perspective in addition to computer science. It highlights the growing importance of description and measurement in political science, alongside the traditional focus on causal inference. The paper also points to the increasing use of machine learning techniques for causal inference in areas like text analysis and political networks. Ultimately, the author suggests that the availability of big data and related tools means that individuals across various fields are now engaged in social science research.
Summary: This piece discusses the intersection of big data, machine learning, and causal inference in the realm of social science. It emphasizes the crucial role of descriptive analysis and the emerging methodologies that combine machine learning with the goal of understanding causal relationships, suggesting a broader engagement with social scientific inquiry in the current data-rich environment
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts” by Justin Grimmer and Brandon M. Stewart
Main Findings: The article emphasizes that automated content analysis methods can significantly reduce the costs and time associated with analyzing large collections of political texts, making previously impossible research feasible. It outlines four key principles for the effective use of these methods, including the understanding that all quantitative models of language are imperfect but useful, these methods augment human analysis rather than replace it, no single method is universally superior, and validation is crucial. The authors survey various supervised (dictionary and supervised learning) and unsupervised (clustering, topic models) methods for classifying and scaling text, providing guidance on their application and validation.
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Summary: This article serves as a guide to the field of automated content analysis for political texts. It highlights the potential of these methods to analyze vast amounts of textual data while also cautioning against their uncritical application. The authors provide a framework for using and evaluating these techniques, covering data acquisition, preprocessing, different analytical methods, and the essential step of validation
Framing Immigration in the Canadian and British News Media” by Andrea Lawlor
Main Findings: This study found that despite differences in immigration levels and national contexts, the Canadian and British broadsheet news media utilized surprisingly similar thematic frames when reporting on immigration between 1999 and 2013. The research identified five key frames inductively: social services, economy/labour, illegality, refugee/asylum, and diversity. The analysis revealed that Canadian media tended to frame immigration in a more event-driven manner, with framing changes often linked to specific events, while British media exhibited more sustained framing patterns. Overall, Canadian coverage was generally more positive in tone than British coverage, with a notable exception in the period following 9/11.
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Summary: This article investigates how immigration was framed in the mainstream print news media of Canada and Britain over a fifteen-year period. Employing automated content analysis, the author identifies the dominant frames used in both countries, examines their frequency and tone over time, and considers the influence of significant political events. The study highlights both the commonalities and differences in how these two immigrant-receiving nations are portrayed in their respective news media.
“Blueprint for Transformation: 2023 Progress Report of the National Advisory Council on Poverty” by the National Advisory Council on Poverty
Main Findings: This report presents the National Advisory Council on Poverty’s assessment of Canada’s progress in poverty reduction in 2023. It reiterates previous recommendations made by the Council to the Government of Canada, urging urgent action in five key areas to create a stronger, simpler, more equitable, and proactive social safety net aimed at reducing poverty and supporting the most vulnerable populations. The Council’s advice is based on an examination of existing data and extensive engagement with individuals who have lived experience of poverty, stakeholders, community organizations, and academic experts.
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Summary: This document is a progress report from the National Advisory Council on Poverty in Canada. It outlines the Council’s perspective on the country’s efforts to reduce poverty, referencing previous recommendations and emphasizing the need for immediate action in specific areas to improve the social safety net. The report’s findings are informed by a combination of data analysis and consultations with a diverse range of stakeholders, particularly those directly affected by poverty
