dataesr/scientific-paragraphs-categorization huggingface dataset:
Background The source of information for this case, included in the WHO database, was published in the International Journal for Environmental Research and Public Health  (Abara et al. , 2014). The town of Graniteville, South Carolina, suffered a chlorine spill which led to deaths and injuries requiring medical attention due to inhalation of chlorine  (Abara et al. , 2014). Whereas past disaster responses did not adequately address community engagement, the chlorine disaster response in Graniteville was different, as the research team from the University of South Carolina, Columbia, from the onset engaged key stakeholders from academic, civil society, private organizations and residents in the response efforts. To that effect, the research team partnered with local stakeholders to identify and address locally identified health and environmental concerns.
Background The source of information for this case, taken from the WHO database, is the Community-Led Ebola Management and Eradication (CLEME) program report prepared by Action Contre la Faim International  (ACF, 2015). This NGO partnered with District Health teams in Sierra Leone to introduce the CLEME approach. During the 2014-2015 Ebola outbreak, ACF collaborated with health authorities and district partners in Moyamba and Kambia. The report describes the rapid early spread of Ebola because communities did not fully adhere to the recommended safety measures, which was attributed to weak social mobilization and community engagement practices  (ACF, 2015). The ACF's CLEME approach employed a five-phase process that included selecting communities and assessing them on the Ebola outbreak and dangers; applying a Participatory Rural Appraisal (PRA) methodology; developing a community-guided action plan; and ensuring long-term safety by following up on the CLEME approach (ACF, 2015).
Background The source of information for this case, maintained in the WHO database, is the Wai Jia Tam's report (2021) on the Migrant Workers Risk Communication & Community Engagement Project, by My Brother SG and the National University of Singapore. Migrant workers, mainly from Bangladesh, India and China, make up a sizable percentage of the Singapore population. They often live in highly overcrowded dormitories with an occupancy rate that far exceeds what is approved by authorities, which makes them vulnerable to COVID-19 infection  (Tam, 2021). According to the report published in August 2020, a local NGO (My Brother SG) and the National University of Singapore initiated a Risk Communication and Community Engagement project to address the specific communication and engagement needs of this migrant worker population.
Since this project involved well-developed health promotion strategies, it focused on more remote communities where the interventions from the government were not as present. The work began with translating risk communication messages into local languages and engaging local people to bring those messages to community members. The team collected baseline data and conducted intervention activities, which included producing an app for household preparedness (which was originally designed by DOH but was not successfully rolled it out in these communities). A total of 8000 community health workers (CHW) were identified and supplied with mobile phone on which an app was installed, connected to the DOH. The team also conducted a 'Train the Trainers' workshop to enable CHWs to support their colleagues in using the app.
Additionally, efforts were made to tap into existing networks, including traditional leaders, healers, religious leaders, artists, teachers and school staff. This group formed a coalition made up of appointed coordinators, which was supplied with personal protective equipment (PPE) and informational materials customized to the local language. They used SMS messages to communicate on mobile devices and local radio to send health messages. Following the interventions, data were collected to trace the effectiveness of the strategy in terms of gains in knowledge, changes in the attitude of the community toward the pandemic, practice of protective behavior (handwashing, wearing face masks, etc. ), and the people's ability to recognize and address misconceptions and misinformation.
Acknowledgement of historic and current realities There are also deeply embedded societal challenges to be acknowledged. As mentioned above, and seen clearly in the responses to Ebola and COVID-19, profound distrust in Western/Northern biomedical advice can be traced back to legacies of colonialism in many parts of the world  (Miller, 2007; Tilley, 2020; Mosby and Swidrovich, 2021). As this distrust cannot be easily surmounted in emergency situations, addressing historic injustice needs to be a part of ongoing health promotion practice  (Leitch et al. , 2021). Another societal challenge concerns the use of social media. While social media may offer many positive applications to deal with health emergencies, they also present certain challenges. Specifically, their role as a catalyst in enhancing the overwhelming, rapid and far-reaching spread of information, and especially in spreading and reinforcing inadequate or misleading information, means that addressing a health crisis also involves a need to develop critical health literacy  (Abel and McQueen, 2020; Sentell et al.
Data transparency The data that support our findings for all studies are openly available in OSF at https://osf. io/ b3u5k/?view_only=1be338fdb1534ca48f121f95b3fa9679. All manipulations, and all exclusions of the three reported studies are reported in the manuscript. All measures are mentioned in the manuscript and described in detail in the S1 File. We performed data analysis with the sample sizes provided herein. No additional data were sought for any of the studies after initial data-analysis. Participants in all studies were randomly assigned to experimental conditions.
A G � Power 3 analysis  [52]  indicated that we needed approximately 375 participants. We requested 450 participants on MTurk. The final sample was N = 384 MTurk workers after exclusion of 60 participants from the original sample (N = 444 complete responses). A sensitivity analysis showed that with N = 384, α =. 05, and 80% desired power, the minimum detectable effect size was an Odd Ratio = 1. 78 (based on a probability under H0, p1 = 0. 5). Exclusion criteria were the following: Participants who did not give informed consent after the debriefing at the end of the experiment (n = 9), who did not pass the attention check (n = 21), who had missing data for measures used in the analyses (n = 1), who indicated a gender other than male or female (n = 4), who indicated they were an MTurk worker as a profession (n = 1), who were not English native speakers (n = 16), and the second trial of those who participated twice due to technical error (MTurk Worker IDs occurring twice, n = 10).
This "wave through approach"  (Campesi 2018 ) enabled them to reduce the number of persons for whom Italy was held liable and to increase pressure on the EU to get more support. In response, the Commission imposed the "Hotspot Approach" (EC 2015). While the promised relocation scheme it foresaw broadly failed (European Council 2017), the deployment of European agencies on Italian and Greek islands succeeded in enforcing the systematization of the transmission of newcomers' fingerprints to the EURODAC (EC 2016a; Heller and Pezzani 2016). As the status quo remained unchanged until now, not less than 91% of the external borders crossings datasets transmitted to the Eurodac since 2003 were performed by Greece, Italy, and Spain alone. 4.
Chart 2: Minimum Delays Before Granting Access to the Labor Market to Asylum Seekers Based on European Migration Network Ad Hoc Queries  (2010, 2013, 2014, 2015 , 2016, 2019) , Asylum Information Database Country Reports and a report on Denmark for the European Commission  (Madsen 2016). Absence of columns means asylum seekers are de jure or de facto prohibited to work. Small ones that there are no foreseen minimum periods. Against this background and in line with the ambition to harmonize asylum policies, the EU legislation rather tends to put limits to those barriers. Noticeably, the reception directive foresaw a maximum time limit of twelve months (The Council 2003a art. 11. 2)-subsequently reduced to nine (European Parliament and The Council 2013a art. 15. 1)-for granting the right to work.
First and only one of its kind, the European Dactyloscopy is a European-wide database which collects the biometric profiles of asylum seekers but also migrants caught at external borders. Negotiated since the beginning of the 1990s, the Eurodac regulation was adopted in 2000   and implemented in 2003  (the Council 2003)). Initially supposed to ease the procedure of determination of the responsible MS for examining an application, its scope has been progressively enlarged: noticeably, since its 2013 recast (European Parliament and the Council, 2013c), it can be used for law enforcement purposes. Its scope is likely to be further extended if the current attempt to reform the CEAS succeeds.
Data and measurement To investigate the above hypotheses and research questions, we will use originally collected data from a two-wave panel survey fielded in 16 European countries and Israel (Austria, Belgium, Denmark, France, Germany, Greece, Hungary, Italy, Netherlands, Norway, Poland, Romania, Spain, Sweden, Switzerland, and the UK). The study was designed as a two-wave survey with the original goal to map contemporary political information environments from an audience perspective. Wave 1 was conducted in December 2019, wave 2 in May and June 2020. This was in the period that most countries under study were ending or no longer had any lockdowns, but still stringent measures limiting social life and public interaction. In light of the COVID-19 pandemic, we repeated several measures in the second wave to investigate change.
The abstract is therefore considered to be a promotional genre, with many optimistic phrases intended to convince the reader of the importance of the work presented by the authors (e. g. : "it could be effective…", "our findings suggest promising effects on…", "this present study could provide a novel insight into…"). We propose a comparative study of the abstracts associated with preprints issued in response to the beginning of the COVID-19 pandemic (from January 1 st to April 12 th , 2020) with those from the closest pre-pandemic period, produced in 2019 (i. e. : from January 1 st to December 31 st , 2019). The construction of a corpus of preprint abstracts enabling this comparison is described in the Methods and Data section and should also be considered as a contribution of the study, especially as it has been made available for further research  (Bordignon et al.
Methods and Data -constitution of a corpus of preprints issued in response to the COVID-19 pandemic (COVID-19 corpus), -delineation of a corpus of preprints produced in 2019, the closest pre-pandemic period (Pre-pandemic corpus), -automatic identification of positive, negative and hedge words with CorTexT Manager (CorTexT Manager documentation, 2020), -data analysis in Excel (to create the source spreadsheets) and Tableau (a data visualization software we used to query the spreadsheets, make our calculations and generate the graphs presented below, see https://www. tableau. com).
Corpus delineation The starting point for our method was Fraser's corpus (2020), which is a corpus of preprints focused on the COVID-19. Since our aim was to identify possible differences between the writings produced during the crisis compared with those produced previously, the delineation of the control corpus was as critical as it was challenging. Furthermore, we also needed to be able to technically retrieve abstracts. As a consequence, although OSF preprints appears as an important server in Fraser's corpus (7th in terms of volume), we had to give up collecting abstracts from this server as they are poorly indexed in both Dimensions (https://www. dimensions. ai) and Lens (https://www. lens. org). After several tests and data retrieval attempts, we were able to build a corpus from the following seven servers: SSRN, arXiv, medRxiv, bioRxiv, Research Square, Preprints.
COVID-19 corpus To locate preprints about COVID-19, Fraser (2020) used the following query hereinafter referred to as COVID query: "coronavirus" OR "COVID-19" OR "sars-cov" OR "ncov-2019" OR "2019-ncov". On April 12th, 2020, we downloaded Fraser's dataset to retrieve the abstracts and other metadata of 3,341 preprints deposited since January 1st, 2020 on the seven different preprint servers mentioned above. The pre-pandemic corpus as the control corpus In order to be sure to base our study on comparable corpora, the control corpus also contained preprints taken from the same preprint servers as the abstracts and for which we tried to delineate the same perimeter. Indeed, if we wanted to determine a shift in writing practices with the advent of the pandemic in the papers released since January 1, 2020, we needed to find papers from 2019 that dealt with similar subjects and which we could be almost sure were written by the same communities.
We therefore developed a strategy for each server depending on the bibliographic databases that index them and the quality of the data available: -SSRN We ran the COVID query in Dimensions over the year 2020 and identified the two most important sub-disciplines (Research categories): 1103 Clinical Sciences and 1117 Public Health and Health Services. We applied this limitation to all preprints indexed in Dimensions for the year 2019. -arXiv We ran the COVID query in Dimensions over the year 2020 and identified the main subdiscipline, 1117 Public Health and Health Services. We applied this limitation to all preprints indexed in Dimensions for the year 2019. -medRxiv Since medRxiv is a disciplinary server, we considered all preprints indexed in Dimensions for 2019 to be within the same scope.
In consequence, we selected all the 2019 preprints of ChemRxiv. All the resulting queries are available online with our data  (Bordignon et al. , 2021). We retrieved the abstracts semi-manually from Dimensions or via the Lens database when available. Some papers had been deposited on several servers; they were counted each time in order to provide a more accurate view per server and, since as they are few in number (1,06% only), this does not modify the main trends in any way. Table  1  shows the distribution of the preprints by server for the COVID-19 corpus and the control (pre-pandemic) corpus.
Approach and methods This study was conducted by a multidisciplinary team composed of social (historians, lawyers, human geographers) and physical (geomorphologists) scientists, supported by research engineers with expertise in statistics, Geographic Information Systems (GIS) and remote sensing techniques. This team performed documentary search and analysis using three points of entry, including (1) natural hazards, risks, disasters; (2) economic development, tax system; and (3) town and country planning, risk management, environmental management. Using these points of entry, team members identified relevant documents and databases in their field of expertise (see SM1 for details), including: 1. Historical archives from mainland France, Guadeloupe and Saint-Martin, which provided information on Saint-Martin's political-   (1947, 1969, 1984, 1989 and 1999   PhD theses on Guadeloupe and Saint-Martin.
Interviewed inhabitants were met spontaneously on the ground and were voluntary to share their personal experience of the TCs. The interviews were conducted in public places not to be intrusive. The narratives of respondents were firstly analyzed individually, and the main common traits between them were secondly drawn. These narratives formed a coherent whole. Data sharing (over the search phase) and analysis involved the setting up of a common 'TEV database'. The latter took the form of a shared table comprising nine columns aimed at gathering, in chronological order and from 1648 (partition of the island) to the Present, relevant information (defined as contributing significantly to the TEV of Saint-Martin COM) on: geopolitics (column 1); politics (2); institutions (3); tax system and finance (4); law (5) related to town and country planning (5.
Panel B focuses on contemporary drivers and illustrates the causal chain linking economic to demographic, sociocultural, town and country planning-related and environmental drivers, as well as the reinforcement of vulnerability and exposure through positive feedback loops (R1-R6). and vulnerabilities (7. 3); town and country planning (8) with a focus on urbanization, infrastructure development and critical services (e. g. water supply, health…); and risk management (9). Concurrently, public databases (data source 3) were analyzed in two different ways. The first way consisted in statistical analysis, used to highlight demographic, economic and societal changes over the period of analysis. The second method used was the setting-up of a GIS database including information on (1) coastal urbanization, based on the extraction of buildings and roads at the five dates covered by aerial photographs  (1947, 1969, 1984, 1989, 1999)   (3) GIS-derived cross-cutting statistical analysis, used to estimate the extent and elevation of buildings and roads at urbanized coastal sites (see location in Fig.
1 ), and the extent of TC Irma-driven flooding. Post-cyclone field trips allowed the collection of observations at all coastal sites. These observations were complementary to satellite image analysis to assess the physical, ecological (on the vegetation cover) and human (on buildings and infrastructure) impacts of the 2017 TCs. The TEV and GIS databases firstly allowed us to reconstruct the TEV of Saint-Martin. This was done by identifying the factors that contributed to the evolution of the system and are a vital link in the causal chain. This led to the formalization (Fig. 2 ) of the long-term and contemporary cause-effect relationships (including reinforcing mechanisms contributing to path dependency) driving changes in exposure and vulnerability over time. These databases were secondly used to analyze the cascading effects of the 2017 TCs, which were formalized using the same methodology (Fig.
Acknowledgements This work was supported by the  French Research Agency  [ NoANR-18-OURA-0002-03 ]. The authors acknowledge support from the  IGN  for providing historical aerial photographs and from the  CNES  for the Airbus and Defence and Space 2017 satellite archive. They warmly thank local and national stakeholders from diverse institutions who helped understanding risk management in Saint-Martin as well as residents who participated in interviews in the post-cyclone context. The authors are grateful to  Alexandre Magnan  for useful comments on the first draft of this article.
e. , personal and interactive engagement) has the potential to improve movie performance, and these two types of engagement can interact to make this effect stronger. We analyze these relationships drawing from the uses and gratifications theory and the social media affordances perspective that motivate customers to interact in social media. The theoretical model evaluation was tested on a unique and original dataset composed of 966 movies released in the U. K. and Spain. The empirical analysis suggests that personal and interactive engagement are positively related to movie performance, and the positive effects of personal and interactive engagement on movie performance are mutually reinforcing. This research contributes to Information Systems (IS) research by theorizing and empirically showing how customer engagement driven by social media creates business value by improving movie performance.
e. , the opening-weekend box office gross revenue). This manuscript theorizes that personal engagement and interactive engagement are positively associated with movie performance and that the positive effect of personal and interactive engagement on performance are mutually reinforcing 2. We check our proposed model "using partial least squares (PLS)-path modeling (PLS-PM) with an extensive secondary dataset"  (Benitez et al. , 2018a, p. 131 ) on a sample of 966 movies in Spain and the U. K. Our theorization and findings provide several contributions to IS research. First, this study focuses on explaining the effect of customer engagement before movies are released on movie performance since the time of release is the largest earning period for most movies. Second, this study considers multiple social media (three of the most used social media sites), focusing on the U.
S. Dollars  (Hopewell, 2019). Meanwhile, according to the U. K. Cinema Association, British cinema admissions reached 177 million in 2018, yielding a total box office of more than 1. 277 million GDP  (Cinemauk. org. uk, 2019). To reach the final sample, we used a manual data screening and collection process as follows: First, we collected the name of all the movies which were planned to be released from different databases, yielding a list of 2,240 movies. Second, once we have collected the movie performance and social media data, we removed movies without movie performance or social media data. Most of these dropped movies had already been released, but they were re-released for a certain movie festival or on video-on-demand platforms such as Netflix or HBO. After dropping these movies, the sample was composed of 1,620 movies.
Third, we removed 637 movies because though we had movie performance and social media data; the movie was re-released (e. g. , for a movie festival or some special occasion such as an award-winning); therefore, the movie performance referred to a past period (i. e. , a mismatch between movie performance and social media data), keeping a total of 983 movies. Finally, we also dropped the movies whose movie performance referred to a different country (i. e. , we had the data for a different country but not for the country studied in this paper), yielding a final sample of 966 movies. To measure the different variables considered in our study, we gathered and leveraged secondary data from eleven sources (i. e. , IMDb (https://www. imdb. com/), Film Affinity (https://www. filmaffinity. com/es/main.
html), Sensacine (http://www. sensacine. com/), Estreno de Cine (https://www. estrenosdecine. net/), Launching Films (https://www. launchingfilms. com/), Movie Insider (https://www. movieinsider. com/), Facebook site (https://www. facebook. com/), Twitter site (https://twitter. com/), YouTube site (https://www. youtube. com/), Tweetreach (https://tweetreach. com/) and Box Office Mojo (https://www. boxofficemojo. com/)) for one year between December 2015 and November 2016. We first collected the name of the movies which were scheduled to be released from different databases. We used IMDb, Film Affinity, Sensacine, and Estreno de cine to collect the movies released in Spain, and we used IMDb, Launching Films, and Movie Insider to obtain the movies released in the U. K. Then, we used each movie's title to collect information about these movies from the rest of the databases.
Personal engagement We draw from  Oh et al. 's (2017)  work to measure personal engagement. The personal engagement was conceptualized as a composite first-order construct shaped by Facebook likes, Twitter followers, YouTube views, and YouTube likes. We collected the data the day before the release date. Facebook likes refer to the movie's number of Facebook likes. Twitter followers were evaluated through the "number of followers on Twitter for each movie profile"  (Oh et al. , 2017, p. 31). YouTube views were specified as the YouTube "count of views for a particular video of each movie profile"  (Oh et al. , 2017, p. 30)  and YouTube likes as the YouTube "count of likes for a particular video of each movie profile"  (Oh et al. , 2017, p. 30 ). These variables are metrics accumulated over time from the creation of the social media profile and the video uploading.