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This study provides insights into the pattern of COVID-19 research publications in Indonesia and aims to map out the contributions of social science scholars in the country during the pandemic, using bibliometric analysis. The analysis was conducted through desk research using the scientometric method from the Scopus database, with a total of 1,037 articles analysed. The research found that social sciences scholars in Indonesia made significant contributions to the fields of online learning, economics, business and management, public health and administration, and communication and media studies. The study also identified the influences of research areas, funding, open access, female first author, female co-authors, and international collaborative research on quartile and the number of citations. The implications of the study suggest that efforts should be made to promote gender diversity in research, allocate research funding appropriately, and encourage international collaboration. Authors should prioritize publishing in high quartile journals and opt for open access publication. Additionally, academic institutions and funding agencies should provide guidance on selecting reputable journals to maintain a culture of quality research.
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This book offers a critique of the present status of the concept of causality in the social sciences. "The Causality Syndrome” consists of a belief in causal studies as more important than other studies, a narrow definition of causality, and rules of thumb regarding how to make causal claims. The book argues that the present dominance of this syndrome has considerable downsides and presents a challenge to social science. The book dissects the many interconnected ideas which undergird this syndrome and offers an intellectual home for advanced students, researchers and others who are concerned about the present dominance of The Causality Syndrome. The book critically discusses whether "causality” deserves the central position in social science that its advocates desire. The text shows how methodological rules about causal inference are used to protect causal studies from critique, even in situations where these rules are not followed. It is argued that institutionalization of these rules as symbols of good and trustworthy social science is highly problematic and comes with a price. One of the casualties of causality is that there is less motivation to study complex and pressing issues in society which do not lend themselves to causal study designs. The sections are short. The argument unfolds in a lively, engaged form with examples from many fields, including public health, evaluation and organizational studies. The case examples include classical experiments as well as contemporary research, e.g. studies of the effectiveness of restrictions targeting the spread of coronavirus. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.
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For scholars based in North America who study various topics in China, the global spread of COVID-19 and resulting travel restrictions imposed by governments and research institutions have erected new barriers to field sites and local contacts. New disease-related travel restrictions are overlaid upon constrained political conditions for some research activities and a general climate of mistrust between the governments of the United States and China observed in recent years. How have scholars responded to these conditions? What research tools do scholars have at their disposal to see their research through? How has COVID-19 compelled or inspired scholars to reconsider their methods, their research subjects, and the ethics of interacting with people in new ways? This chapter assembles insights from a diverse group of scholars working in North American institutions who are re-tooling their China-related research during these times of great uncertainty. They discuss new methods to allow for continued engagement and note the limitations and possibilities that the pause in fieldwork presents for generating new knowledge. The overall picture is a frank assessment of a research landscape characterized by unprecedented constraints and new types of risks which may persist well into the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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PURPOSE: To assess how previous experiences and new information contributed to COVID-19 vaccine intentions. DESIGN: Online survey (N = 1264) with quality checks. SETTING: Cross-sectional U.S. survey fielded June 22-July 18, 2020. SAMPLE: U.S. residents 18+; quotas reflecting U.S. Census, limited to English speakers participating in internet panels. MEASURES: Media literacy for news content and sources, COVID-19 knowledge; perceived usefulness of health experts; if received flu vaccine in past 12 months; vaccine willingness scale; demographics. ANALYSIS: Structural equation modelling. RESULTS: Perceived usefulness of health experts (b = .422, P < .001) and media literacy (b = .162, P < .003) predicted most variance in vaccine intentions (R-squared=31.5%). A significant interaction (b = .163, P < .001) between knowledge (b = -.132, P = .052) and getting flu shot (b = .185, P < .001) predicted additional 3.5% of the variance in future vaccine intentions. An increase in knowledge of COVID-19 associated with a decrease in vaccine intention among those declining the flu shot. CONCLUSION: The interaction result suggests COVID-19 knowledge had a positive association with vaccine intention for flu shot recipients but a counter-productive association for those declining it. Media literacy and trust in health experts provided strong counterbalancing influences. Survey-based findings are correlational; thus, predictions are based on theory. Future research should study these relationships with panel data or experimental designs.
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Misinformation in the media is produced by hard-to-gauge thought mechanisms employed by individuals or collectivities. In this paper, we shed light on what the country-specific factors of falsehood production in the context of COVID-19 Pandemic might be. Collecting our evidence from the largest misinformation dataset used in the COVID-19 misinformation literature with close to 11,000 pieces of falsehood, we explore patterns of misinformation production by employing a variety of methodological tools including algorithms for text similarity, clustering, network distances, and other statistical tools. Covering news produced in a span of more than 14 months, our paper also differentiates itself by its use of carefully controlled hand-labeling of topics of falsehood. Findings suggest that country-level factors do not provide the strongest support for predicting outcomes of falsehood, except for one phenomenon: in countries with serious press freedom problems and low human development, the mostly unknown authors of misinformation tend to focus on similar content. In addition, the intensity of discussion on animals, predictions and symptoms as part of fake news is the biggest differentiator between nations; whereas news on conspiracies, medical equipment and risk factors offer the least explanation to differentiate. Based on those findings, we discuss some distinct public health and communication strategies to dispel misinformation in countries with particular characteristics. We also emphasize that a global action plan against misinformation is needed given the highly globalized nature of the online media environment. Supplementary Information: The online version contains supplementary material available at 10.1007/s42001-022-00193-5.
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In this paper, we introduce a project on singles' intimate practices conducted during COVID times as a case study of quantitative social research with a particular focus on qualitative reflections. We thematize the topic of self-reflexivity, which is considered an essential category in qualitative research but largely neglected in quantitative research. We discuss three methodological issues through the lens of self-reflection: 'translation issues';the problems of asking 'sensitive' and the 'right' questions;and the problematics of 'the present' in particularly fluid times. We show that this approach promotes contextualization of the measurement tool, the data and the findings and can be a way for doing quantitative research on intimacy outside the 'standard' nuclear family in pandemic times. Overall, this paper underscores the ways that scholars as individuals and teams are inextricable from our research site, as we navigate disruption even while seeking to understand its implications on our informants. [ FROM AUTHOR] Copyright of International Journal of Social Research Methodology is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Contention over COVID-19 is only a recent example of increasing social division around science in the U.S. Many blame these divisions on actors who have strategically sowed doubt and distrust around expert supported positions and policies. However, this overlooks how scientists have fueled narratives of social and political conflict around science. This study explores how science influencers on social media have used group identity language in ways that may perpetuate narratives of intergroup conflict around science. Using computer-assisted content analytic methods, we examine how science influencers' use of group identity language has changed in response to recent events (Trump presidency, COVID-19 pandemic) and across different social media platforms (Twitter, Facebook, Instagram). While there are slight increases in group identity language between 2016 and 2021, different patterns across platforms suggest that science influencers use different platforms to perform multiple roles of engaging diverse audiences, building ingroup solidarity, and defending against outgroup criticism. © 2023 Taylor & Francis Group, LLC.
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Objective To analyze the research status and development trend of evidence-based social sciences, and to explore the synergistic relationship among branches of various fields. Methods A comprehensive search for research related to evidence-based social sciences published between the establishment of the database and September 2022 was conducted on the Web of Science, China Science Citation Database (CSCD), and Chinese Social Sciences Citation Index (CSSCI). The VOSviewer software was used to analyze the author and keywords and to perform the visualization. Results A total of 6 969 papers were included, 195 of which were in Chinese. The first paper, published in 1995, was on evidence-based management. The number of papers published on evidence-based social science research presented a trend of increasing continuously. The country with the most papers published was the United States;the institution outside China with the most papers published was McMaster University;and the institution in China with the most papers published was Sichuan University. The analysis of keywords showed that evidence-based policy, evidence-based practice, evidence-based management, and evidence-based decision-making appeared more frequently;research questions focused on evidence-based health policy development, application of big data in the context of COVID-19, evidence of climate change-induced disease, and real world research. Conclusion All the fields of evidence-based social sciences are closely related to each other in terms of research content and methods, and it presents a multi-level and multi-field crossover with evidence-based medicine. However, evidence-based social sciences in China are still in their infancy. Therefore, it is necessary to improve the methodological system of evidence-based social sciences, enrich the research content of social sciences, speed up the filling of evidence gaps in various fields, and promote the improvement and sustainable development of evidence-based social sciences.Copyright © 2023 West China University of Medical Science. All rights reserved.
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The COVID-19 pandemic has affected various aspects of our lives. For many, it has affected their ability to attend school. While some have switched to online classes, others have had to drop or delay college until later. Using official enrollment data for 12 public universities in the State of Texas, this study explores the impact the COVID-19 pandemic has had on student enrollment in criminal justice programs. A series of statistical techniques, including t-tests comparing pre- and post-pandemic enrollment numbers and panel data analysis models, are utilized to investigate the trends and changes in the program enrollments between 2009 and 2021. While in alignment with the existing research on the effect of the COVID-19 pandemic on college enrollment in general the authors have found a negative statistically significant effect of the pandemic on total college enrollment for all universities in the sample, no statistically significant effect of the pandemic was found on enrollment in criminal justice programs at 12 public universities. The effect was also non-existent for engineering and all social science programs combined. In contrast to all other programs studied herein, enrollment in natural science programs was found to be positively associated with the pandemic. Authors offer an explanation for these findings as well as suggest ideas for future research. Copyright © 2023 Korotchenko and Dobbs.
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This work introduces a simple extension to the recent Cognitive Cascades model of Rabb et al. with modeling of multiple media agents, to begin to investigate how the media ecosystem might influence the spread of beliefs (such as beliefs around COVID-19 vaccination). We perform some initial simulations to see how parameters modeling audience fragmentation, selective exposure, and responsiveness of media agents to the beliefs of their subscribers influence polarization. We find that media ecosystem fragmentation and echo-chambers may not in themselves be as polarizing as initially postulated, in the absence of outside fixed media messages that are polarizing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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More than 40 years after the Alma-Ata Declaration on Primary Health Care, it is time to take stock. A look back at the evolution of pharmaceutical policies reveals the extent to which international health has transformed in the last four decades. The imperative of equitable access to healthcare, reaffirmed in Astana in 2018, has still not been achieved in many countries across the globe, whereas recent Ebola and COVID epidemics have opened up new political spaces for pharmaceutical development. In response to a gap in the literature with regard to the politics behind global pharmaceutical policymaking, we offer a critical interpretive synthesis of the literature on pharmaceutical policies, in English and French, from 1978 to 2018 inclusively. Our search strategy and inclusion criteria enabled us to select and review 134 papers and books on pharmaceutical policies. Building upon the seminal works of K.S. Rajan, we review the literature under the following assumption: pharmaceutical policies reflect or enact different conceptions of knowledge, political spaces, and value. We then critically discuss our findings in light of the contemporary debates, particularly in the wake of recurring epidemics. We thereby challenge the mainstream perspective according to which pharmaceuticals and pharmaceutical policies must be viewed as value-free, apolitical instruments. © 2022 The Authors
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Attitudes are often expressed in what people say and write, as well as the content they choose to interact with. With the proliferation of social media and other online content, we are able to understand how people express their attitudes through large-scale linguistic analyses. Further, people's attitudes and behaviors are often intertwined: attitude signals can be predictive of future behaviors, and conversely behavioral patterns can reveal underlying attitudes. This thesis explores the development of computational linguistic models to understand attitudes and behaviors. We surface the attitudes that people hold with respect to social roles (e.g., "professor," "mother") and compare them across different cultures using corpus-statistics models and dependency-based embedding models. Next, we look at how personal traits are predictive of behavior. To this end, we explore how we can incorporate implicit world knowledge into language models by predicting attitudes towards charitable giving. In this same direction, we examine traits, as expressed on social media, that are indicative of people likely to persist in pursuing self-improvement. We leverage linguistic characteristics such as expressed affect, writing style, and latent topics. Finally, we gain insight into how attitude and behavior give insight to each other by predicting attitudes towards philanthropic causes based on engagement behavior with newsletters and personal background information, using text-aware graph representation models. We also show how behavioral traits present in online communities are predictive of resilient attitude during the COVID-19 pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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COVID-19 has destabilized economies, lives all over the globe with devastating impacts. Physical and mental health suffered hugely due to this pandemic. Moreover, educational institutes were forced to shift from offline to online mode. This study aims to identify critical societal constructs which got impacted due to the COVID-19 pandemic in India and Bangladesh. Further, a comparative analysis has been carried out to assess these impacts between these two countries from a social science point of view. A cross-sectional study has been conducted through a structured questionnaire based on pilot study. Google Survey form link of questionnaire was active from March 15, 2020 to July 14, 2020 in which 1023 responses were received with a response rate of 54%. After the elimination of missing responses, 802 complete responses were finally coded on 48 variables including demographic profile of the respondents. Factor analysis is used to identify critical constructs of the study whereas hypothesis testing to carry out for comparative understanding. The study found that COVID-19 pandemic has severely impact on psychological, educational, economic, and social aspects of both countries whereas Bangladesh has significant adverse impact than India. Economic stimulus program such as tax exemption, EMI moratoriums, mental health training, E-learning, low interest rate of soft loan disbursement are noteworthy requirement to overcome the adverse impact of COVID-19.
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The pandemic has produced profound changes in teaching activities, with consequences on the educational processes and social development of students. In this sense, the following contribution explores the potential of Advanced Functional Didactics (DAF), a didactic proposal that aims to deliver traditional didactic contents through motor activity. Carried out in a difficult social context, namely the IC Ristori of Naples, located in the Forcella district, the study involves a second class of primary school and a first class of lower secondary school. The experimental hypothesis of this research is that the introduction of the DAF can bring benefits to the physical, psychological, social and learning abilities of the learners. This pilot study was the first to carry it out, thus proposing to verify the impact of this methodology on students to collect useful responses to improve it.Alternate :La pandemia ha prodotto profondi cambiamenti nelle attività didattiche, con conseguenze sui processi formativi e di sviluppo sociale degli studenti. In tal senso, il seguente contributo esplora le potenzialità della Didattica Avanzata Funzionale (DAF), una proposta didattica che mira ad erogare i contenuti didattici tradizionali attraverso l'attività motoria. Realizzato in un contesto sociale difficile, ossia l'IC Ristori di Napoli, sito nel quartiere di Forcella, lo studio coinvolge una classe seconda della scuola primaria e una classe prima della scuola secondaria di primo grado. L'ipotesi sperimentale di questa ricerca è che l'introduzione della DAF possa apportare benefici alle capacità fisiche, psicologiche, sociali e di apprendimento dei discenti. Questo studio pilota è stato il primo a realizzarla, proponendosi quindi di verificare l'impatto di questa metodologia sugli studenti allo scopo di raccogliere risposte utili a migliorare quest'ultima.
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On Twitter, COVID-19 is a highly discussed topic. People worldwide have used Twitter to express their viewpoints and feelings during the pandemic. Previous research has focused on particular topics such as the public's sentiment during the lockdown, their opinion on governmental measures, or their stance towards COVID-19 vaccines. However, until today, there is no comprehensive overview that presents possible areas of application for sentiment analysis of COVID-19 Twitter data. Therefore, this study reveals how sentiment analysis can provide relevant insights for managing the pandemic by applying a behavioral and social science lens. In this context, our systematic literature review focuses on machine learning-based sentiment analysis techniques and compares the best-performing classification algorithms for COVID-19-related Twitter data. We performed a search in five databases, which are: IEEE Xplore DL, ScienceDirect, SpringerLink, ACM DL, and AIS Electronic Library. This search resulted in 40 papers published between October 2019 and January 2022 that used sentiment analysis to evaluate the public opinion on COVID-19-related topics, which we further investigated. Our research indicates that the best performing models in terms of accuracy are ensemble models that comprise various machine learning classifiers. Especially BERT and RoBERTa models provide the most promising results when fine-tuned on Twitter data. Our study aims to combine machine learning-based sentiment analysis and insights from social and behavioral science to provide decision-makers and public health experts with guidance on the application of sentiment analysis in the fight against the spread of COVID-19. © 2013 IEEE.
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The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals' and communities' thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions. © 2020 Tsinghua University Press.
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Objective To analyze the research status and development trend of evidence-based social sciences, and to explore the synergistic relationship among branches of various fields. Methods A comprehensive search for research related to evidence-based social sciences published between the establishment of the database and September 2022 was conducted on the Web of Science, China Science Citation Database (CSCD), and Chinese Social Sciences Citation Index (CSSCI). The VOSviewer software was used to analyze the author and keywords and to perform the visualization. Results A total of 6 969 papers were included, 195 of which were in Chinese. The first paper, published in 1995, was on evidence-based management. The number of papers published on evidence-based social science research presented a trend of increasing continuously. The country with the most papers published was the United States;the institution outside China with the most papers published was McMaster University;and the institution in China with the most papers published was Sichuan University. The analysis of keywords showed that evidence-based policy, evidence-based practice, evidence-based management, and evidence-based decision-making appeared more frequently;research questions focused on evidence-based health policy development, application of big data in the context of COVID-19, evidence of climate change-induced disease, and real world research. Conclusion All the fields of evidence-based social sciences are closely related to each other in terms of research content and methods, and it presents a multi-level and multi-field crossover with evidence-based medicine. However, evidence-based social sciences in China are still in their infancy. Therefore, it is necessary to improve the methodological system of evidence-based social sciences, enrich the research content of social sciences, speed up the filling of evidence gaps in various fields, and promote the improvement and sustainable development of evidence-based social sciences.Copyright © 2023 West China University of Medical Science. All rights reserved.
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Objective To analyze the research status and development trend of evidence-based social sciences, and to explore the synergistic relationship among branches of various fields. Methods A comprehensive search for research related to evidence-based social sciences published between the establishment of the database and September 2022 was conducted on the Web of Science, China Science Citation Database (CSCD), and Chinese Social Sciences Citation Index (CSSCI). The VOSviewer software was used to analyze the author and keywords and to perform the visualization. Results A total of 6 969 papers were included, 195 of which were in Chinese. The first paper, published in 1995, was on evidence-based management. The number of papers published on evidence-based social science research presented a trend of increasing continuously. The country with the most papers published was the United States;the institution outside China with the most papers published was McMaster University;and the institution in China with the most papers published was Sichuan University. The analysis of keywords showed that evidence-based policy, evidence-based practice, evidence-based management, and evidence-based decision-making appeared more frequently;research questions focused on evidence-based health policy development, application of big data in the context of COVID-19, evidence of climate change-induced disease, and real world research. Conclusion All the fields of evidence-based social sciences are closely related to each other in terms of research content and methods, and it presents a multi-level and multi-field crossover with evidence-based medicine. However, evidence-based social sciences in China are still in their infancy. Therefore, it is necessary to improve the methodological system of evidence-based social sciences, enrich the research content of social sciences, speed up the filling of evidence gaps in various fields, and promote the improvement and sustainable development of evidence-based social sciences. © 2023 West China University of Medical Science. All rights reserved.
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Scholars increasingly use Twitter data to study the life sciences and politics. However, Twitter data collection tools often pose challenges for scholars who are unfamiliar with their operation. Equally important, although many tools indicate that they offer representative samples of the full Twitter archive, little is known about whether the samples are indeed representative of the targeted population of tweets. This article evaluates such tools in terms of costs, training, and data quality as a means to introduce Twitter data as a research tool. Further, using an analysis of COVID-19 and moral foundations theory as an example, we compared the distributions of moral discussions from two commonly used tools for accessing Twitter data (Twitter's standard APIs and third-party access) to the ground truth, the Twitter full archive. Our results highlight the importance of assessing the comparability of data sources to improve confidence in findings based on Twitter data. We also review the major new features of Twitter's API version 2.