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Recent literature models leadership as a process of communication in which leaders’ rhetorical signals facilitate followers’ co-ordination. While some studies have explored the effects of leadership in experimental settings, there remains a lack of empirical research on the effectiveness of informational tools in real political environments. Using quantitative text analysis of federal and sub-national legislative addresses in Russia, this article empirically demonstrates that followers react to informational signals from leaders. It further theorizes that leaders use a combination of informational and non-informational tools to solve the co-ordination problem. The findings show that a mixture of informational and non- informational tools shapes followers’ strategic calculi. Ignoring non-informational tools — and particularly the interrelationship between informational and non-informational tools - can threaten the internal validity of causal inference in the analysis of leadership effects on co-ordination.
Coding non-manifesto documents as if they were genuine policy platforms produced at election time clearly raises serious issues with error when these codings are used in the standard manner to estimate left-right policy positions. In addition to the long term solution of improving the document base of the Manifesto Project identified by Gemenis (2012), we argue that immediate gains in manifesto-based estimates of policy positions can be realised by using the confrontational logit scales from Lowe et al. (2011), which addresses the problems of scale content and scale construction that are exacerbated by but not unique to the problems found in proxy documents.
Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models require the additional step of attaching meaningful labels to estimated topics, a process that is not scalable, suffers from human bias, and is difficult to replicate. We present a transfer topic labeling method that seeks to remedy these problems, using domain-specific codebooks as the knowledge base to automatically label estimated topics. We demonstrate our approach with a large-scale topic model analysis of the complete corpus of UK House of Commons speeches from 1935 to 2014, using the coding instructions of the Comparative Agendas Project to label topics. We evaluated our results using human expert coding and compared our approach with more current state-of-the-art neural methods. Our approach was simple to implement, compared favorably to expert judgments, and outperformed the neural networks model for a majority of the topics we estimated.
The scarcity of empirical evidence surrounding the organizational challenges and successful approaches to artificial intelligence (AI) deployment has resulted in mostly theoretical conceptualizations. By analyzing policy labs and offices of data analytics across the US to understand organizational challenges of AI adoption and implementation in the public sector as well as to identify successful management strategies to address such challenges, our study moves from speculation to gathering evidence. Our findings show that most challenges are found during the implementation stage and include challenges related to skills, culture, and resistance to share the data driven by data challenges. Further, our results indicate that long term strategies and short term actions need to be put in place to address these challenges. Among the first ones, leadership and executive support and stakeholder management seem to play an important role. Data standardization, training, and data-sharing agreements also seem to be successful specific short-term actions.
There has been substantial focus on China’s influence in Africa in recent years. Some argue that China’s growing economic ties with African states have increased its political influence across the continent. This article examines whether trade with China leads African states to adopt more similar foreign policy preferences to China in the United Nations. We examine foreign policy similarity using voting patterns in the United Nations General Assembly and country statements in the United Nations General Debate. The analysis demonstrates that more trade with China has paradoxical effects on foreign policy positions of African states—it leads them to align more closely with US foreign policy positions in the United Nations, except on human rights votes. Our findings suggest that African states are engaged in balancing behavior with external powers whereby African elites seek to play off rival powers against one another in order to strengthen their own autonomy and maximize trade.
Political science, and social science in general, have traditionally been using computational methods to study areas such as voting behavior, policy making, international conflict, and international development. More recently, increasingly available quantities of data are being combined with improved algorithms and affordable computational resources to predict, learn, and discover new insights from data that is large in volume and variety. New developments in the areas of machine learning, deep learning, natural language processing (NLP), and, more generally, artificial intelligence (AI) are opening up new opportunities for testing theories and evaluating the impact of interventions and programs in a more dynamic and effective way. Applications using large volumes of structured and unstructured data are becoming common in government and industry, and increasingly also in social science research. This chapter offers an introduction to such methods drawing examples from political science. Focusing on the areas where the strengths of the methods coincide with challenges in these fields, the chapter first presents an introduction to AI and its core technology - machine learning, with its rapidly developing subfield of deep learning. The discussion of deep neural networks is illustrated with the NLP tasks that are relevant to political science. The latest advances in deep learning methods for NLP are also reviewed, together with their potential for improving information extraction and pattern recognition from political science texts.
Background: Instituted under the Paris Agreement, nationally determined contributions (NDCs) outline countries’ plans for mitigating and adapting to climate change. They are the primary policy instrument for protecting people’s health in the face of rising global temperatures. However, evidence on engagement with health in the NDCs is scarce. In this study, we aimed to examine how public health is incorporated in the NDCs, and how different patterns of engagement might be related to broader inequalities and tensions in global climate politics.
Methods: We analysed the NDCs in the UN Framework Convention on Climate Change registry submitted by 185 countries. Using content analysis and natural language processing (NLP) methods, we developed measures of health engagement. Multivariate regression analyses examined whether country-level factors (eg, population size, gross domestic product [GDP], and climate-related exposures) were associated with greater health engagement. Using NLP methods, we compared health engagement with other climate-related challenges (ie, economy, energy, and agriculture) and examined broader differences in the keyword terms used in countries with high and low health engagement in their NDCs.
Findings: Countries that did not mention health in their NDCs were clustered in high-income countries, whereas greater health engagement was concentrated in low-income and middle-income countries. Having a low GDP per capita and being a small island developing state were associated with higher levels of health engagement. In addition, higher levels of population exposure to temperature change and ambient air pollution were associated with more health coverage included in a country’s NDC. Variation in health engagement was greater than for other climate-related issues and reflected wider differences in countries’ approaches to the NDCs.
Interpretation: A focus on health in the NDCs follows broader patterns of global inequalities. Poorer and climate-vulnerable countries that contribute least to climate change are more likely to engage with health in their NDCs, while richer countries focus on non-health sectors in their NDCs, such as energy and the economy.
Objective: To examine country engagement with the health impacts of climate change in (1) annual statements in the UN General Debate (UNGD); and (2) the Nationally Determined Contributions (NDCs) of the Paris Agreement; and to identify what factors drive country engagement.
Methods: First, we measure engagement by the total references to the health and climate change relationship in each text, using a keyword-in-context search with relevant search terms. Second, we use machine learning models, specifically random forest models, to identify the most important country-level predictors of engagement. Our predictors are political and economic factors, health outcomes, climate change-related variables, and membership of political negotiating groups in the UN.
Findings: For both UNGD statements and NDCs, we find that that low- and middle-income countries discuss the health impacts of climate change much more than high-income countries. We find that the most important predictors of country engagement are related health outcomes (infant mortality rates, maternal death risk, life expectancy), countries’ income levels (GDP per capita), and fossil fuel consumption. Membership of political negotiating groups (e.g. G77 and SIDS) is less important predictors.
Conclusion: Our analysis indicates a North-South division in engagement, and hence there is little to suggest that a health framing of climate change overcomes existing geopolitical divisions in climate change negotiations. Countries who carry the heaviest health burdens but lack necessary resources to address the impacts of climate change are shouldering responsibility for reminding the global community of the implications of climate change for people’s health.
AI for SDG-16 on Peace, Justice, and Strong Institutions: Tracking Progress and Assessing Impact
(2019)
The transition from the Millennium Development Goals (MDGs) to the Sustainable Development Goals (SDGs) brought with it significant changes in the process of creating the goals and with the actual content of the SDGs. One of the most important developments was the inclusion of SDG 16, which recognises the central role of effective, accountable and inclusive political institutions in promoting sustainable development. Yet, a significant shortcoming is the difficulty in measuring progress on this SDG 16. In addition to general issues linked with data availability across the various indicators, a key challenge is aggregating trends across these wide-ranging indicators to track overall progress on SDG 16. A second issue that follows, is that despite claims regarding the centrality of SDG 16 for achieving the other SDGs, little is known about the causal pathways from the different indicators in SDG 16 to the other SDGs and associated indicators. In other words, questions remain over how changes in SDG 16 indicators impact a country’s progress towards indicators linked to health, gender equality, water and sanitation, and climate change.
All methods for analyzing text require the identification of a fundamental unit of analysis. In expert-coded content analysis schemes such as the Comparative Manifesto Project, this unit is the ‘quasi-sentence’: a natural sentence or a part of a sentence judged by the coder to have an independent component of meaning. Because they are subjective constructs identified by individual coders, however, quasi-sentences make text analysis fundamentally unreliable. The justification for quasi-sentences is a supposed gain in coding validity. We show that this justification is unfounded: using quasi-sentences does not produce valuable additional information in characterizing substantive political content. Using natural sentences as text units, by contrast, delivers perfectly reliable unitization with no measurable loss in content validity of the resulting estimates.