Suchergebnisse

AbstractHow do we measure religious violence? This study is focused on utilizing new methodological approaches and data sources to measure religiously motivated violence. Previous attempts to measure religious violence concentrated on coding U.S. State Department International Religious Freedom reports or utilizing existing datasets on armed conflict/civil wars. These previous attempts provided state-level data of the levels of religiously motivated violence, but due to data limitations cannot provide more fine-grained measures of specific acts of violence tied to religious motivation. In particular, accounting for varying levels of intensity especially in regards to non-lethal acts of religiously motivated violence is missing. This study builds upon previous attempts focusing on the creation of more fine-grained measures and accounting for its variation at the sub-national level utilizing natural language processing. The data generated are used to examine incidences of reported religious violence in India from 2000 to 2015.

AbstractThis essay updates the small amount of formal research dedicated to explicating the factors that drive the selection of a vice presidential nominee. Demographic and political characteristics of the individuals on the presidential nominees' short lists, as well as various measures of presidential ticket balance, are modeled for the 24 contested major party vice presidential nominations from 1960 through 2020. Discrete choice analysis highlights the idea that the calculus used by presidential nominees to select their running mate has become more complex in the modern era. Years served in national political office, exposure in the national media, bringing either gender or racial/ethnic diversity to the ticket, and youth are all factors that seem to matter in the selection process. Predicted probabilities generated from the model correctly identify 18 (75%) of the eventual nominees.

Does INGO climate shaming translate into actual climate laws, or is it ineffective in altering the behavior of governments? This article provides the first systematic assessment of whether and under what conditions INGO climate shaming can influence national climate policymaking. Drawing on social movement and NGO literatures, we argue that INGO climate shaming can incur reputational costs for governments through two main pathways: public opinion and transnational politics. To test our propositions, we generate a unique dataset on INGO climate shaming, utilizing natural language processing (NLP) to extract INGO climate shaming events from media sources, covering the period 1990–2020. We find that climate shaming is generally effective in pushing government to introduce climate laws. Particularly, we find that climate shaming is consequential when governments are trade-dependent and have committed to global climate norms. Our findings provide substantive implications for the global climate governance literature.

AbstractScholars contend that the reason for stasis in human rights measures is a biased measurement process, rather than stagnating human rights practices. We argue that bias may be introduced as part of the compilation of the human rights reports that serve as the foundation of human rights measures. An additional source of potential bias may be human coders, who translate human rights reports into human rights scores. We first test for biases via a machine-learning approach using natural language processing and find substantial evidence of bias in human rights scores. We then present findings of an experiment on the coders of human rights reports to assess whether potential changes in the coding procedures or interpretation of coding rules affect scores over time. We find no evidence of coder bias and conclude that human rights measures have changed over time and that bias is introduced as part of monitoring and reporting.

AbstractHuman trafficking affects millions of people globally, disproportionately harming women, girls and marginalized groups. Yet one of the main sources of data on global trafficking, the annual Trafficking in Persons (TIP) Reports, is susceptible to biases because report rankings are tied to political outcomes. The literature on human rights measurements has established two potential sources of bias. The first is the changing standards of accountability, where more information and increased budgets change the standard to which countries are held over time. The second is political biases in reports, which are amended to comply with the interests of the reporting agency. This letter examines whether either of these biases influence the TIP Reports. In contrast to other country-level human rights indicators, the State Department issues both narratives and rankings, which incentivizes attempts to influence the rankings based on political interests. The study uses a supervised machine-learning algorithm to examine how narratives are translated into rankings, to determine whether rankings are biased, and to disentangle whether bias stems from changing standards or political interests. The authors find that the TIP Report rankings are more influenced by political biases than changing standards.

This manuscript helps to resolve the ongoing debate concerning the effect of information communication technology on human rights monitoring. We reconceptualize human rights as a taxonomy of nested rights that are judged in textual reports and argue that the increasing density of available information should manifest in deeper taxonomies of human rights. With a new automated system, using supervised learning algorithms, we are able to extract the implicit taxonomies of rights that were judged in texts by the US State Department, Amnesty International, and Human Rights Watch over time. Our analysis provides new, clear evidence of change in the structure of these taxonomies as well as in the attention to specific rights and the sharpness of distinctions between rights. Our findings bridge the natural language processing and human rights communities and allow a deeper understanding of how changes in technology have affected the recording of human rights over time.

Sentiment, judgments and expressed positions are crucial concepts across international relations and the social sciences more generally. Yet, contemporary quantitative research has conventionally avoided the most direct and nuanced source of this information: political and social texts. In contrast, qualitative research has long relied on the patterns in texts to understand detailed trends in public opinion, social issues, the terms of international alliances, and the positions of politicians. Yet, qualitative human reading does not scale to the accelerating mass of digital information available currently. Researchers are in need of automated tools that can extract meaningful opinions and judgments from texts. Thus, there is an emerging opportunity to marry the model-based, inferential focus of quantitative methodology, as exemplified by ideal point models, with high resolution, qualitative interpretations of language and positions. We suggest that using alternatives to simple bag of words (BOW) representations and re-focusing on aspect-sentiment representations of text will aid researchers in systematically extracting people's judgments and what is being judged at scale. The experimental results below show that our approach which automates the extraction of aspect and sentiment MWE pairs, outperforms BOW in classification tasks, while providing more interpretable parameters. By connecting expressed sentiment and the aspects being judged, PULSAR (Parsing Unstructured Language into Sentiment-Aspect Representations) also has deep implications for understanding the underlying dimensionality of issue positions and ideal points estimated with text. Our approach to parsing text into aspects-sentiment expressions recovers both expressive phrases (akin to categorical votes), as well as the aspects that are being judged (akin to bills). Thus, PULSAR or future systems like it, open up new avenues for the systematic analysis of high-dimensional opinions and judgments at scale within existing ideal point models.

How does the discussion of human rights issues change over time? Without advocates adopting a human rights issue in the first place, international 'shaming' cannot occur. In this article, we examine how human rights discussions converge and diverge around new frames and new issues over time. Human rights norms do not evolve alone; their prevalence, framing, and focus are all dependent on how they relate to other norms in the advocacy community. Drawing on over 30,000 documents from dozens of human rights organizations from 1990 to 2011, we provide a temporal overview and visualization of the ebb and flow of human rights issues. Using our new dataset and state-of-the-art methods from computer science, our approach allows us to quantitatively examine (a) how new issues emerge in the advocacy network, (b) the relationship of these new issues to extant human rights advocacy and information, and (c) how the framing and specificity of these issues change over time. By focusing on the process by which a new issue gets incorporated into the work of advocates, we provide an empirical assessment of the first step in the causal process connecting shaming to improvement in human rights practices.