Social Science Texts Research Articles

A corpus-based study of live grammatical metaphor in English academic writing

ABSTRACT Nominalization is an important resource for creating grammatical metaphor (GM). Previous research shows that nominalization is characteristic of academic writing but does not demonstrate discipline sensitivity within the academic genre. The reason might be that some nominalizations have developed into dead GM. This study aims to identify nominalizations that create live GM and explore the discipline sensitivity of live GMs based on a corpus of academic article abstracts across three discipline groups. The findings indicate that a clause construes an event or a status, which is either conveyed by the verbal group or a nominal group functioning as a participant of the verbal group. Verb nominalizations construing events and adjective nominalizations construing statuses are the key factors in identifying live GM. Notably, nominalizations not cooccurring with verbalizations are not necessarily dead GMs. The corpus-based research shows that both verb and adjective nominalizations exhibit discipline sensitivity. Verb nominalizations are more prevalent in natural science texts, whereas adjective nominalizations are more prevalent in the humanities and social science texts. However, live GMs are not inherently discipline sensitive; rather, it is those GMs contributing to the lexical density of the text that are discipline sensitive.

Advancing language models through domain knowledge integration: a comprehensive approach to training, evaluation, and optimization of social scientific neural word embeddings

This article proposes a comprehensive strategy for training, evaluating, and optimizing domain-specific word2vec-based word embeddings, using social science literature as an example. Our primary objectives are: (1) to train the embeddings utilizing a corpus of social science text, (2) to test their performance against domain-unspecific embeddings using our developed intrinsic and extrinsic evaluation strategy, and (3) to enhance their performance even further by using domain knowledge. As an integral part of this approach, we present SociRel-461, a domain-knowledge dictionary designed for the intrinsic evaluation and subsequent refinement of social science word embeddings. Using a dataset of 100,000 full-text scientific articles in sociology, we train multiple vector space models, which we then benchmark against a larger, pre-trained general language embedding model as part of our extrinsic evaluation. Furthermore, we developed a transfer learning multi-label classification task for extrinsic evaluation. Our findings reveal that domain-specific embeddings outperform their domain-unspecific counterparts in both intrinsic and extrinsic evaluations. We also investigated the retrofitting post-processing method to enhance domain-unspecific embeddings with the domain knowledge embedded in SociRel-461. While retrofitting does not enhance our domain-specific vector space models, it significantly improves the performance of the domain-unspecific embeddings. This highlights the potential of retrofitting for the transfer of domain knowledge to domain-unspecific embeddings. Our results emphasize the importance of utilizing domain-specific word embeddings for better performance in domain specific transfer learning tasks, as they outperform conventional embeddings trained on everyday language.

Exploring named‐entity recognition techniques for academic books

Recent advances in the natural language processing (NLP) field have achieved impressive results in various tasks. However, NLP techniques are underrepresented in the analysis of Humanities and Social Science texts and in languages other than English. In particular, academic books are a highly valuable source of information that has not been exploited by these techniques at all. The recognition of named entities (person names, organizations or locations) and their semantic annotation over books could enrich the visibility and discoverability of the information by users. This is an opportunity for academia and the academic publishing industry in which semantic search is a central task and now books can be queried by named entities of interest that are in their content. This work proposes a methodology to apply named‐entity recognition to publish the results into an ontological semantic‐web format. The work has been performed over a corpus of academic books provided by UNE (Unión de Editoriales Universitarias Españolas, Union of Spanish University Presses). Results show an enrichment of the information extracted over the books and of the possibilities of querying them at the individual level but also within the whole set of books, increasing the possibilities for books to be discovered or retrieved beyond metadata.

Politics of translation: Assimilation and reflexivity in the transformation of academic texts

For many at the periphery and semi-periphery of the global academic field, translation or self-translation into English has become a prerequisite for the pursuit of an academic career. This article focuses on the academic translators who translate humanities and social science texts and the crucial but highly invisible role of their everyday translation practices in relation to the dominance of Anglophone discourses and concepts. It articulates an interdisciplinary theorization of the politics of translation through the concepts of assimilatory and reflexive translation, which are applied to an empirical investigation based on semi-structured interviews conducted with academic translators in the context of Spain. The analysis brings to light their backgrounds, professional profiles, and perceptions of their working conditions, as well as their views of science and good writing. Academic translators predominantly adopt an assimilatory translation strategy, reformulating texts to adapt them to Anglophone academic conventions. However, habitual collaboration between translators and authors gives rise to deliberation among both actors on how to recreate texts in the new linguistic situation. Such collaboration holds great potential for the development of more reflexive forms of translation in which an invitation to consider translation strategies and decisions is extended to authors and users of translations at large.

Practices in the translation and editing of humanities and social science texts for publication in English: A qualitative survey of language professionals

In 2006, the American Council of Learned Societies published the Guidelines for the Translation of Social Science Texts. Following a panel discussion at the annual conference of Mediterranean Editors and Translators on their continued relevance in an international publishing landscape increasingly populated by writers who use English as an additional language (EAL) a qualitative survey consisting of 10 open-ended questions was conducted in 2020 through several European language professional associations. At that time, the extent to which the practices of translators into English and editors working directly with EAL authors in the humanities and social sciences conformed to the self-reflexive methods of the Guidelines was not known. The 52 responses from language professionals based in European countries (44), North America (7), and Israel (1) were coded to obtain qualitative data on professionals’ opinions and practices. Respondents’ self-identifications were roughly divided between translators and editors, but further examination revealed 25 in fact working as translator–editors, 14 as editors, and 13 as translators. Most respondents engaged in some form of communication with the authors of the source texts regarding their translating/editing choices, although the strategies varied. Most attempted to retain the authors’ ‘voice’ but also felt the competing need to comply with prevailing expectations of international academic discourse in English. This article presents themes that emerged from the survey, which revealed both compliance with and divergence from the Guidelines across diverse academic and linguistic contexts, suggesting the need for updated guidelines and additional research on these mostly occluded author–language professional practices.

SsciBERT: a pre-trained language model for social science texts

The academic literature of social sciences records human civilization and studies human social problems. With its large-scale growth, the ways to quickly find existing research on relevant issues have become an urgent demand for researchers. Previous studies, such as SciBERT, have shown that pre-training using domain-specific texts can improve the performance of natural language processing tasks. However, the pre-trained language model for social sciences is not available so far. In light of this, the present research proposes a pre-trained model based on the abstracts published in the Social Science Citation Index (SSCI) journals. The models, which are available on GitHub (https://github.com/S-T-Full-Text-Knowledge-Mining/SSCI-BERT), show excellent performance on discipline classification, abstract structure–function recognition, and named entity recognition tasks with the social sciences literature.

INTERPRETATION OF SOCIAL SCIENCE TEXTS: TEACHER AND THE CLASSROOM

The given work is an attempt to analyze how social science textbooks based on NCF 2005 with the proposed epistemic shift in the approach of teaching of social sciences have been able to represent a scope for change. More specifically the given paper analyzes the interpretation 0f social science texts to bring in the importance of text book analyses with the purpose of exploring textbook as tool for change. It suggests how the interpretation of social science texts may provide a scope for understanding the changing role of textbooks in classroom teaching learning process. Keeping the proposed epistemic shift in mind, analysis of the concept of citizenship in the series of Social Political Life NCERT textbooks in comparison to colonial Civics, the given paper attempts to give new insights in the vision of pedagogy of Social Science.

Three Gaps in Computational Text Analysis Methods for Social Sciences: A Research Agenda

ABSTRACT We identify three gaps that limit the utility and obstruct the progress of computational text analysis methods (CTAM) for social science research. First, we contend that CTAM development has prioritized technological over validity concerns, giving limited attention to the operationalization of social scientific measurements. Second, we identify a mismatch between CTAMs’ focus on extracting specific contents and document-level patterns, and social science researchers’ need for measuring multiple, often complex contents in the text. Third, we argue that the dominance of English language tools depresses comparative research and inclusivity toward scholarly communities examining languages other than English. We substantiate our claims by drawing upon a broad review of methodological work in the computational social sciences, as well as an inventory of leading research publications using quantitative textual analysis. Subsequently, we discuss implications of these three gaps for social scientists’ uneven uptake of CTAM, as well as the field of computational social science text research as a whole. Finally, we propose a research agenda intended to bridge the identified gaps and improve the validity, utility, and inclusiveness of CTAM.

Chekhov’s gun and Narrative Topography in Social Science Texts

SummaryA social science text has topography. Idea, concepts, and data are structured in a narrative form and the writer faces three challenges: 1) how to deal with the complexity of knowledge in a linear form of writing, 2) how to transform experience and the wealth of data you needed to know to write, into focused and lean writing that the reader needs to read, and finally 3) how, while providing a synthetic representation of reality, to establish memorable credibility of the data and narrative. These challenges can be addressed and overcome by the use of devices we can identify most clearly in art.

A Study on the Techniques of the E-C Long-Sentence Translation in Our Wealth Is Loving Each Other: Self and Society in Fiji under the Theory of Communicative Translation

Long sentences are commonly used in social science texts (SST). According to Peter Newmark’s classification of text types, SST is content-based informative text. Our Wealth Is Loving Each Other: Self and Society in Fiji belong to SST whose main function is to convey information to readers. The present writer exemplifies the E-C translation of long sentences in SST under the guidance of Newmark’s Communicative Translation Theory, hoping to explore the translation techniques of long sentences in this type of texts. The present writer proposes five translation techniques, in an effort to achieve a fluent and faithful translation.

Lexical Features of Text Complexity: the case of Russian academic texts

The work presented in this paper is a part of an ongoing project that investigates academic text features indicative of its complexity at different grade levels. In this study we examine comparative complexity of Social science texts used in Russian secondary and high schools. Based on the metrics of ten descriptive and four lexical features assessed for seven classroom textbooks we claim lexical diversity, frequency, abstractness and the number of terminological units to be statistically significant predictors of text complexity. The total size of the Corpus of over 160.000 tokens comprising two sets of textbooks ranging from the 5th to the 11th grades provides a satisfactory level of its representativeness and as such a solid foundation for statistical validity of the results. We employ RusAC, an online text analyzer, to compute lexical features of texts and the effect of the four lexical features on text complexity is confirmed with a mixed analysis of variance. The study fills a gap both in corpus linguistics as regards a systematic approach to Russian academic texts and in text complexity studies as regards the description of secondary and high school textbooks.

Integrating LSA-based hierarchical conceptual space and machine learning methods for leveling the readability of domain-specific texts

AbstractText readability assessment is a challenging interdisciplinary endeavor with rich practical implications. It has long drawn the attention of researchers internationally, and the readability models since developed have been widely applied to various fields. Previous readability models have only made use of linguistic features employed for general text analysis and have not been sufficiently accurate when used to gauge domain-specific texts. In view of this, this study proposes a latent-semantic-analysis (LSA)-constructed hierarchical conceptual space that can be used to train a readability model to accurately assess domain-specific texts. Compared with a baseline reference using a traditional model, the new model improves by 13.88% to achieve 68.98% of accuracy when leveling social science texts, and by 24.61% to achieve 73.96% of accuracy when assessing natural science texts. We then combine the readability features developed for the current study with general linguistic features, and the accuracy of leveling social science texts improves by an even higher degree of 31.58% to achieve 86.68%, and that of natural science texts by 26.56% to achieve 75.91%. These results indicate that the readability features developed in this study can be used both to train a readability model for leveling domain-specific texts and also in combination with the more common linguistic features to enhance the efficacy of the model. Future research can expand the generalizability of the model by assessing texts from different fields and grade levels using the proposed method, thus enhancing the practical applications of this new method.

Undergraduate Research at Two-Year Community Colleges

ABSTRACTThere is a growing movement in academia that focuses on increased efforts at undergraduate research. Historically, this movement has been driven by faculty in the science, technology, engineering, and mathematics (STEM) fields and has only recently become a focus for social sciences in general and political science in particular. For students to be successful at conducting undergraduate research, they should be exposed to it as soon as they are ready. This requires exposing students to undergraduate research in the freshmen and sophomore years. There is no reason this exposure should be limited to four-year institutions. To that end, a new journal has been created for students in political science and other social science courses at two-year colleges so they can be recognized for their undergraduate research. The Social Science Text and Academic Research (STAR) Journal is a peer/faculty-reviewed journal limited to students at two-year colleges.

A semi-automatic approach for detecting dataset references in social science texts

Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain.We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles.We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.

Family Feuds Are Forever

A follow-up reflection on the writing of “Family Feuds are Forever” explores how writing autoethnographically is not an innocent practice. When we write, we become vulnerable to others’ readings of our words and lived experiences. Yet, as writers of social science texts, we welcome this vulnerability, and in doing so, we discard our innocence. We create interpretations of events and a record for ourselves, and others, that are subject to outside and inside scrutiny. This reflection explores how writing autoethnographically as in “Family Feuds are Forever” challenges us to be ethical with ourselves and others.

The international circulation of social science knowledge

The paper intends to develop a framework for the understanding of theoretical and epistemological aspects of international circulation of social science knowledge in the specific form of social science texts. It problematizes a series of intrinsic parameters that influence the acceptance or rejection of a given circulating text beyond its historical, political, economic and cultural contexts of production. Obviously, geo-political, economic, political and ideological constellations, institutional structures, educational systems and book markets, differentiated and stratified audiences, funding opportunities and networks, the classic domains of history and sociology of science and of STS, affect knowledge-production and circulation. Such arguments, however, suggest that the concerned approaches are not accepted or rejected because of their content but because of their assumed political instrumentalization, or because of the material support that enabled their imposition and domination. The intention of this paper, instead, is to provide a programmatic outline for a serious consideration of text-intrinsic parameters, i.e. of the knowledge-dimension itself and its efficacy, and the specific ways in which they affect circulation of social science texts.

(المضامين السياسية والاجتماعية لقيم التسامح في الكتب المدرسية للمرحلة الأساسية في اقليم كوردستان العراق(دراسة تحليلية

(المضامين السياسية والاجتماعية لقيم التسامح في الكتب المدرسية للمرحلة الأساسية في اقليم كوردستان العراق(دراسة تحليلية

Características lingüísticas de los textos de ciencias sociales en la formación del profesorado

This article raises the need of knowing textual and linguistic characteristics of the social science texts related with the teacher training. The aim has been to assess the importance of knowing “the grammar of the exposition” of social science texts to teachers and students in order to get a better understanding and expression when they write these types of texts.

Anlamların Açılımı: Nitel Görüşme İçeren Çalışmalara Dönük Bazı Yaklaşımlar

Inside interpretivist paradigm, the repertoire of approaches have been enlargened in the last two decades with the popularity of qualitative research practice. While this present new opportunities to craft better social science texts, it also confuses scientists especially when they are in the process of forming research designs in their early field work experience. Based on my past research experience, during and after my doctoral studies, this paper suggests a set of ideas that might clarify some basic considerations regarding descriptive studies which attempt to unfold people’s understandings through interview conversations. For this purpose, the study discusses some selected considerations regarding episthemological stance, method and textual possibilities. Informed mainly by hermeneutics, the set of ideas presented here aims at providing the readers with the qualities that leads to genuine mode of understanding, sharing with them information on how traditional concepts of “validity” and “analysis” can apply to qualitative studies, and making suggestions on the possibility of crafting richer texts for qualitative interview studies.

The Library of Affecting Social Science

A “list” of social science texts that move us and continually captivate our minds and emotions.