Fundamental Assumptions in Narrative Analysis: Mapping the Field

The richness of narrative analysis resides in its unruly openness, but points of reference are needed to tame the variety in the field. This article suggests that researchers should grapple with two fundamental questions when conducting narrative analysis. The first pertains to the status attributed to narrative: it is defined as the very fabric of human existence or as one representational device among others? Emphasizing one answer over the other means mobilizing different theories of representation and therefore, suggesting different articulations between narrative and reality. The second question refers to the perspective developed on narrative: Is it defined mostly as the characteristic of an approach, an object of investigation or both? Different methodological implications are associated with that choice. The article claims that dominant trends in narrative analysis originate in the way researchers answer those two questions

How the EU shapes and hones its identity through the language of its treaties

How does the European Union shape and hone its identity? Odelia Oshri and Shaul Shenhav (Hebrew University of Jerusalem) decipher the ways in which the EU's discourse on values has changed throughout the 60 years of integration. They show that two values dominated the Union's treaty texts - 'democracy' and 'market economy'. However, since the 1990s, new values have penetrated the discourse. ..

Still united in diversity? The longer a country is part of the EU, the stronger its citizens support liberal democratic values

What effect does EU membership have on the values of citizens? Drawing on recent research, Odelia Oshri, Tamir Sheafer and Shaul Shenhav assess the extent to which the EU has been successful in instilling the democratic values in its own citizens that it claims to promote externally. The research demonstrates a strong connection between a state’s duration of EU membership and the degree to which its citizens adhere to liberal democratic values, suggesting that while multiple national identities exist across the EU, it is nevertheless possible to unite citizens under the umbrella of democratic ideology

CompRes: A Dataset for Narrative Structure in News

This paper addresses the task of automatically detecting narrative structures in raw texts. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to news articles, motivated by their growing social impact as well as their role in creating and shaping public opinion. We introduce CompRes -- the first dataset for narrative structure in news media. We describe the process in which the dataset was constructed: first, we designed a new narrative annotation scheme, better suited for news media, by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success); then, we used that scheme to annotate a set of 29 English news articles (containing 1,099 sentences) collected from news and partisan websites. We use the annotated dataset to train several supervised models to identify the different narrative elements, achieving an $F_1$ score of up to 0.7. We conclude by suggesting several promising directions for future work.Comment: Accpted to the First Joint Workshop on Narrative Understanding, Storylines, and Events, ACL 202

Genome-wide assessment of post-transcriptional control in the fly brain

Post-transcriptional control of gene expression has central importance during development and adulthood and in physiology in general. However, little is known about the extent of post-transcriptional control of gene expression in the brain. Most post-transcriptional regulatory effectors (e.g., miRNAs) destabilize target mRNAs by shortening their polyA tails. Hence, the fraction of a given mRNA that it is fully polyadenylated should correlate with its stability and serves as a good measure of post-transcriptional control. Here, we compared RNA-seq datasets from fly brains that were generated either from total (rRNA-depleted) or polyA-selected RNA. By doing this comparison we were able to compute a coefficient that measures the extent of post-transcriptional control for each brain-expressed mRNA. In agreement with current knowledge, we found that mRNAs encoding ribosomal proteins, metabolic enzymes, and housekeeping genes are among the transcripts with least post-transcriptional control, whereas mRNAs that are known to be highly unstable, like circadian mRNAs and mRNAs expressing synaptic proteins and proteins with neuronal functions, are under strong post-transcriptional control. Surprisingly, the latter group included many specific groups of genes relevant to brain function and behavior. In order to determine the importance of miRNAs in this regulation, we profiled miRNAs from fly brains using oligonucleotide microarrays. Surprisingly, we did not find a strong correlation between the expression levels of miRNAs in the brain and the stability of their target mRNAs; however, genes identified as highly regulated post-transcriptionally were strongly enriched for miRNA targets. This demonstrates a central role of miRNAs for modulating the levels and turnover of brain-specific mRNAs in the fly

Citizenship Traditions and Cultures of Military Service:Patriotism and Paychecks in Five Democracies

Why do people think that soldiers and officers join the military? In this article, we report and explain unique survey results of nationally representative populations in five democracies—France, Germany, Israel, the United Kingdom, and the United States. Beliefs about motivations for military service vary significantly by nation. In Israel and France, large majorities endorse intrinsic accounts of service motivations—that is, those centering on patriotism and good citizenship. The U.S. population is nearly evenly split between extrinsic accounts—ascribing service to the pay and benefits received or to the desire to escape desperate circumstances—and intrinsic ones. A large majority of U.K. and Germany-based respondents hew to extrinsic service accounts. We argue that the most plausible explanation lies with prevailing national citizenship discourses, in combination with the military’s operational tempo. This research has implications for public support for military recruitment, the use of force, and democratic civil–military relations.<br/

Storytelling in den Vereinten Nationen: Mahbub ul Haq und menschliche Entwicklung

Ausgehend von der Beobachtung, dass Mitarbeiter der Vereinten Nationen eine wichtige Rolle in Prozessen des ideellen Wandels auf internationaler Ebene spielen können, beschäftigt sich dieser Beitrag mit einer bestimmten Form individuellem Einflusses – dem storytelling. Mein Verständnis von storytelling als Einflusstaktik kombiniert dabei kollektive Elemente der soziologischen Praxistheorie mit den reflexiven, akteursbezogenen Überlegungen von Michel de Certeau. Ich analysiere storytelling anhand von drei analytischen Elementen: einem (chronologischen) Plot, einer Reihe von Charakteren und einem interpretativen Thema – die jeweils ihre Wirkung im Zusammenspiel mit der Subjektivität ihres storytellers entfalten. Ich illustriere diese theoretischen Überlegungen mit dem Fall von Mahbub ul Haq, dem es als Sonderberater des United Nations Development Programme (UNDP)-Administrators zu Beginn der 1990er Jahre gelungen ist, die Idee der menschlichen Entwicklung im System der Vereinten Nationen und der internationalen Entwicklungspolitik zu etablieren

A Weakly Supervised and Deep Learning Method for an Additive Topic Analysis of Large Corpora

The collaborative effort of a theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. Additivity also enables the reuse of previous efforts or the merging of separate research projects, thereby increasing the accessibility of such methods and the ability of the discipline to create shareable content analysis capabilities. This paper proposes a weakly supervised topic analysis method, which combines a low-cost unsupervised method to compile a training-set and supervised deep learning as an additive and accurate text classification method. We test the validity of the method, specifically its additivity, by comparing the results of the method after adding 200 categories to an initial number of 450. We show that the suggested method is a solid starting point for a low-cost and additive solution for a large-scale topic analysis

Role-based association of verbs, actions, and sentiments with entities in political discourse

A crucial challenge in measuring how text represents an entity is the need to associate each representative expression with a relevant entity to generate meaningful results. Common solutions to this problem are usually based on proximity methods that require a large corpus to reach reasonable levels of accuracy. We show how such methods for the association between an entity and a representation yield a high percentage of false positives at the expression level and low validity at the document level. We introduce a solution that combines syntactic parsing, semantic role labeling logic, and a machine learning approach—the role-based association method. To test our method, we compared it with prevalent methods of association on the news coverage of two entities of interest—the State of Israel and the Palestinian Authority. We found that the role-based association method is more accurate at the expression and the document levels