dataesr/scientific-paragraphs-categorization huggingface dataset:
In countries with weak commitments to democracy, incumbents commonly make dubious charges of fraud when they do not win  (Schedler 2001) , and their supporters often believe them  (Cantu and Garcıa-Ponce 2015). Such claims are uncommon in consolidated democracies, making Donald Trump's blatant lies all the more noteworthy and abnormal  (Anderson et al. 2005 ). 2  The goal of this article is to study how lies shape voters' perceptions about election integrity, support for violence, us  (Bright Line Watch 2020; 2021; Clayton et al. 2021; Drutman 2021; Pennycook and Rand 2021). The ELTS not only complements these surveys but also provides a richer dataset in key respects. In this article, we further develop the concept of the big lie by integrating research on cults of personality in authoritarian contexts with research on misinformation in democratic contexts.
Data We administered the ELTS online through the Qualtrics survey platform; this established and respected online survey research firm recruits participants and verifies their names, addresses, and dates of birth before inviting them to join their sampling panels. It incentivizes participation in surveys by compensating respondents with money or money equivalents, such as Amazon credits, and it conducts validity checks of responses to produce a highquality sample. Importantly, the data quality and representativeness of Qualtrics samples have been independently verified by scholars to be in line with probability samples like the General Social Survey and the American National Election Studies  (Boas, Christenson, and Glick, 2020). We restricted the population to US citizens who are registered to vote.
In the one-month period from November 16 to December 15, we surveyed 1,558 Republican voters, 941 (60. 4%) of whom refused to identify Biden as the election winner. Based on this estimate, as well as the data Low levels of political interest could explain this lack of updating: voters might not have been paying attention to the Supreme Court decision on December 11 to reject the Texas lawsuit, or the Electoral College vote a few days later, or McConnell's subsequent endorsement of Biden's win. But even the most salient events like Biden's inauguration did not increase acceptance of the outcome. Moreover,  Pennycook and Rand (2021)  found that Trump voters who were the most knowledgeable about politics were more likely to believe the election was stolen. Our intuition is that, by the end of the election, the reluctance to acknowledge the Biden win among Republicans was likely driven more by motivated reasoning, selective exposure to slanted news coverage, and perhaps a degree of "expressive responding" than by low political awareness  (Osmundsen et al.
n = 1,245 Note: The table displays the responses to a question that was shown to respondents who did not identify Joe Biden as the winner of the election even after the race had been called. The question was only included in the survey from November 16 to December 15, 2020. All data drawn from Election Legitimacy Tracking Survey (ELTS). this mindset changed substantially after the election result was secured by the Electoral College vote, the events of January 6, and Democrats regained control of the White House and the Senate; yet support for violence among Democrats remained significantly higher than preelection levels even after Biden was inaugurated. Figure  5  explores precisely which members of the US population believe radical political action is most justified. The relationships that emerge are the same for Democrats, Republicans, and unaffiliated voters.
Letters mark significant political events: D = Election Day, Nov. 3; M = race called by news networks, Nov. 7; L = Trump invites Michigan legislators to White House, Nov. 24; B = Barr says no evidence of fraud, Dec. 2; E = Electoral College certifies Biden, Dec. 15; C = Capitol insurrection, Jan. 7; I = Inauguration Day, Jan. 20. Line segments represent 95% confidence intervals. All data drawn from Election Legitimacy Tracking Survey (ELTS).
Acknowledgment Our gratitude goes to the  University Center for Human Values  and  Data Driven Social Science Initiative at Princeton University  for funding support. Additional financial support was provided by  Temple University. We are grateful for helpful feedback from the editor, three anonymous reviewers,  David Broockman ,  Nathan Kalmoe ,  Romain Lachat ,  Yphtach Lelkes ,  Tali Mendelberg ,  Markus Prior , and participants at the  Séminaire Général de CEVIPOF at Sciences Po.
Notes: Letters mark significant political events: D = Election Day, Nov. 3; M = race called by news networks, Nov. 7; L = Trump invites Michigan legislators to White House, Nov. 24; B = Barr says no evidence of fraud, Dec. 2; E = Electoral College certifies Biden, Dec. 15; C = Capitol insurrection, Jan. 7; I = Inauguration Day, Jan. 20. Line segments represent 95% confidence intervals. All data drawn from Election Legitimacy Tracking Survey (ELTS).
All data drawn from Election Legitimacy Tracking Survey (ELTS). Outcome: Believes Biden Won Outcome: Believes Biden Won Proportion Identifying Biden as Winner Proportion Identifying Biden as Winner 0. 00 0. 25 0. 50 0. 75 1. 00. 11-06. 11-09. 11-12. 11-15. 11-18. Democrat. 11-06. 11-09. 11-12. 11-15. 11-18. 11-24 12-01 12-03 12-04 12-08 12-11 12-15 12-18 12-22 01-08 01-10 01-12 01-15. 11-06. 11-09. 11-12. 11-15. 11-18. 11-24 12-01 12-03 12-04 12-08 12-11 12-15 12-18 12-22 01-08 01-10 01-12 01-15. 11-06. 11-09. 11-12. 11-15. 11-18. 11-24 12-01 12-03 12-04 12-08 12-11 12-15 12-18 12-22 01-08 01-10 01-12 01-15 DM E B L C 11-24 01-22 01-19 01-26 01-29 01-19 01-22 01-26 01-29 01-19 01-22 01-26 01-29 I 12-01. 11-06. 11-09. 11-12. 12-04. 11-15. 11-18. 11-06. 11-09. 11-12. 11-15. 11-18. 11-06. 11-09.
We searched the archives and in particular the web archives of the scientific and state institutions involved in monitoring and risk management. We collected all the written documents. By the end of our period of study, we had collected 320 items including press releases, scientific bulletins, news on websites and public notes (Table  1 ; a table listing all the documents we collected during our period of study is provided in the Supplement). Hereafter, we are citing scientific bulletins and websites as references (including their URL when available), while authorities' press releases are given in the dataset in the Supplement (press releases are typically from the prefecture of Mayotte, but there are also a few press releases from the government and from ministries). We did not consider the numerous automatic bulletins released by REVOSIMA (daily automatic bulletins have been released since March 2020), BCSF-RéNass, and EMSC, but we included the report published by the BCSF-RéNass's Groupe d'intervention macrosismique (GIM) and a web article from the EMSC aiming at providing a global view of the seismic crisis.
We also included in our database the five academic papers (one was a preprint version of a submitted paper) dedicated to the crisis that were published during our period of study  (Cesca et al. , 2020; Famin et al. , 2020; Feuillet et al. , 2021b; Lemoine et al. , 2020; Tzevahirtzian et al. , 2021)  and commented on by the press and/or the members of STTM Facebook group. We also took into account the contribution of individual researchers who issued key analyses at crucial times during the crisis  (Briole, 2018). Each item was downloaded, stored in pdf under a specific ID, and then read independently by two to three researchers who completed a table with information about format and content. Disagreements were discussed and solved collectively. We took note of the ID, the date of publication, the URL (when available), the publishing authors/institutions, the title, the public it was aimed at, the number of words, the presence or absence of illustrations, and the nature of these illustrations  (scientific, local, etc.
). We also took note of the main topics covered by the text and of the list of actors that were mentioned. This dataset was used to quantify the volume and timing of public information and to undertake a qualitative analysis of the content. To complete our understanding of the public information process, we also explored Facebook publication feeds when they existed (i. e. , for OVPF-IPGP, REVOSIMA, and prefecture of Mayotte) but without aiming for exhaustiveness as it was difficult to achieve without adequate tools. Table  1. Format and volume of the documents made public by the main actors of scientific monitoring and risk and crisis management during our period of study. A table listing all the documents we collected during our period of study is provided in the Supplement. As discussed in the text, we only count a report and a web article for, respectively, the BCSF-RéNass and the EMSC and not their automatic reports.
Examining the process of public information with regard to what is known of at-risk populations' information needs The combination of these data (archives, interviews, notes of participant observation, written documents used by the actors to share information with the public) provided the basis for examining the public information process with regard to what is known of at-risk populations' information needs. The latter is inferred from the existing literature on risk communication (which is abundant on this particular topic; see Sect. 7) while bearing in mind the social and cultural context of Mayotte. We also explored STTM's Facebook publication feed but, again, without aiming for exhaustiveness as it was difficult to achieve without adequate tools. Hereafter, we use excerpts from STTM Facebook posts to illustrate some of our statements.
] that encourages the scientific community to endeavour, alongside their existing technical expertise, to '. support action by local communities and authorities; and support the interface between policy and science for decision-making' " (Sendai framework p. 22 -UNISDR, 2015). As shown in this paper, this change in expectations creates new challenges for scientists, notably on the issue of communication. We hope that this work will contribute to open new leads for transdisciplinary research drawing on geosciences, social sciences, and humanities that can improve the effectiveness of the sciencesociety nexus for disaster risk reduction. Data availability. EMSC data on the felt seismicity are available from https://doi. org/10. 5281/zenodo. 4734032 (EMSC-CSEM, 2021). Instrumental seismicity plotted in Fig.
1  is from  Lemoine et al. (2020)  dataset and from the REVOSIMA catalog (not yet available for distribution, these data will be included in  Saurel et al. , 2021). A table listing all the written documents issued by the scientific and state institutions involved in monitoring and risk management is provided in the Supplement. The press releases from the prefecture of Mayotte and French ministries that we refer to in the text are given in full in the dataset in the Supplement. The French version of STTM post excerpts are also provided. Full verbatim interviews from which we extracted cited excerpts are not public for confidentiality. All other data used in this paper are available from cited references. Supplement. The supplement related to this article is available online at: https://doi.
org/10. 5194/nhess-22-2001-2022-supplement. Author contributions. MHD was responsible for the conceptualization of the study, project administration, methodology, and writing the original draft of the paper. MHD and RL undertook the revision and editing of the final paper in concert with all co-authors. MHD and GR were responsible for data curation and investigation. RL curated the STTM Facebook threads and selected relevant excerpts. MHD and GR conducted and transcribed the interviews. MHD, RL, and GR undertook the formal analysis. MHD and RL carried out the validation. HP, RL, and MHD were responsible for the figures. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. This work benefited from the data of felt seismicity collected by the EMSC (https://doi. org/10. 5281/zenodo. 4734032) whose activities are supported by the Fondation SCOR pour la Science. The authors would also like to thank Rémy Bossu, Laure Fallou, and Matthieu Landès from EMSC, who provided very useful comments at different stages of the work. People interviewed during these 3 years are also to be thanked for their invaluable contribution to our thinking process. The authors also want to thank Emmelyne Mitard, communication officer in IPGP, scientists and officials from the DIRMOM, the prefecture of Mayotte, and the REVOSIMA for their help in setting up exhaustive records on the communication elements put in place by the scientists and the authorities during the course of the crisis.
Many thanks also to Aline Peltier, who chairs the scientific committee of REVOSIMA and kindly agreed to read and comment on the paper before its submission. Finally, thanks to Louise Le Vagueresse and Hugo Pierrot, whose work helped us to check the completeness of our communication database. Financial support. This research has been supported by the IdEx Université de Paris, Centre des Politiques de la Terre (grant no. ANR-18-IDEX-0001). Review statement. This paper was edited by Amy Donovan and reviewed by Anna Hicks and Julie Morin.
Data collection We conducted a survey in Australia, France, Germany, and South Africa in May of 2021. Respondents were recruited from an online panel maintained by Qualtrics, using quotas to represent as closely as possible national populations in terms of gender, age, income, and ethnicity. Doing so, non-probability voluntary sampling was employed to make our questionnaire available to potential respondents. Respondents were informed that completion of the survey implied consent to use their anonymous data, and no personal details allowing identification of respondents were requested. As per the guidelines of the first author's university, research for which there is no foreseeable risk of harm or discomfort; and any foreseeable risk is no more than inconvenience; and that involves the use of records that contain only non-identifiable data about human beings is exempt from ethics review.
According to study power calculations, we needed a minimum of 385 respondents per country sample. Considering this threshold and the available funding, we aimed to recruit approximately 500 respondents per country. The final sample consisted of 494 respondents from Australia, 549 from France, 510 from Germany, and 547 from South Africa, for a total of 2,100 respondents completing the survey and providing a valid response in terms of meeting standard data quality requirements such as not providing the same answer for each question or highly implausible answers. Individual country samples approximate their respective national statistics in terms of socio-demographics (Table  1 ) and COVID-19 statistics (Table  2 ). Exceptions to this overall pattern are an oversampling of affluent individuals in Australia and South Africa, and an oversampling of individuals suspecting to have been infected with COVID-19 but not having been formally diagnosed and those reporting COVID-19 infections within the household in South Africa.
Methods The extraction of Twitter data is carried out using the open-source python package snscrape. The application is developed with Dash, an open-source framework for building web interactive applications entirely in Python. The dashboard allows the user to create a dataset, without coding, according to four criteria: the text query, the desired time range, the number of tweets to be retrieved and the language of the tweets. The dashboard provides three tabs with (i) navigation across the dataset, and its exportation in CSV format, (ii) a time analysis with the count of tweets for each day and for each hour of the day, and (iii) a text analysis with a word 1 Corresponding Author, Antoine Lamer, ULR 2694, 2 place de Verdun, F-59000, Lille, France; Email: antoine. lamer@univ-lille. fr.
These five jurisdictions are all unitary and centralized states, unlike federal states such as Germany or the United States, and therefore, each "national" broadsheet newspaper can be representative of the jurisdiction. In addition, using the abovementioned robot density figures (38) as a proxy, the sample of these five jurisdictions has a good spread, with low and high robot production levels. The five newspapers were: Le Monde (France), The Times (Great Britain), Ming Pao Daily News ( in Chinese, Hong Kong SAR), The Irish Times (Ireland) and Yomiuri Shimbun ( in Japanese, Japan). These five broadsheet newspapers were selected based on the high volume of circulation in each jurisdiction, combined with a relatively high level of trust in the international community and the data availability covering the period.
It is worth noting however that amongst many care robots that have been developed, those introduced into rehabilitation and telework for people with disabilities began to provide concrete examples of their usefulness not only for users themselves but also for wider society. There is great potential for achieving a more inclusive society if care robots are appropriately implemented and used to empower users and their carers. This research has several limitations. We only used a single source from each jurisdiction, and there could be a greater variety of data if we collected it from multiple newspapers. The fact that Ireland had only a few articles does not necessarily mean the general public has no interest in care robots in Ireland. In fact, as previously noted, Stevie the Irish robot made the cover of Time magazine.
This rule restricts the rights according to a specific period of time https://doi. org/10. 1016/j. jisa. 2021. 103006 and a number of accesses, two aspects that only can be managed with UCON. Therefore, integrating UCON in software and system design tools, including business process modeling tools such as BonitaSoft  [7] , jBPM  [8] , and Activiti  [9]  that currently do not support the modeling of complex security policies, as the ones enabled by UCON, would be a major step forward in the definition and implementation of secure information systems. One first attempt to deal with UCON policies was published in  [10]  where an extension of artifact-centric business process models to deal with partial UCON policies was proposed. However, the solution was not generic and only useful for that specific type of models and, moreover, UCON expressiveness was not completely supported, and the metamodel was not formalized.
In this paper, a domain-specific language for specifying UCON policies is proposed. This language is generic and fully compliant with the UCON specification, enabling definition and integration of UCON policies in any type of model. As such, as far as we know, ours is the first proposal that presents a UCON metamodel that covers the whole access control model and that is flexible enough to be added to any modeling toolchain. In other words, our approach aims at providing MDE tools to implement the original UCON. Additionally, our approach covers not only the specification of UCON access control rules but also their enforcement by leveraging the same modeling infrastructure we used to create the language in the first place. Model-Driven Engineering (MDE)  [11]  promotes the rigorous use of software and systems models in all software engineering activities and also facilitates the development process, the code generation, and the integration in different environments  [12, 13].
Policy enforcement is explored by means of an application-independent structure based on a Policy Enforcement Point (PEP)-Policy Decision Point (PDP) architecture. This architecture is implemented by using model transformations, concretely, the ATL model transformation language and framework  [14]  are proposed to evaluate rules at run-time. Furthermore, to illustrate the usability of the proposed Domain-Specific Language, a complex running example that includes UCON policy rules related to both the processes and the data model has been employed. The rest of the paper is structured as follows: Section 2 introduces the necessary concepts to understand the UCON model, the model-driven engineering, and the ATL model transformation language; Section 3 introduces the running example that illustrates the need for defining policies with a UCON-based language; Section 4 introduces the model-driven approach and its application to the motivating example; Section 5 goes over the most relevant previous papers related to access control; and, finally, conclusions are drawn and further works are pointed out.
Background This section is structured as follows: Firstly, Section 2. 1 introduces the main concepts behind the Usage Control Access Model (UCON). Afterwards, Section 2. 2 gives a general overview of model-driven engineering and the notion of domain-specific language, and then focuses on model transformations as the tool in the model-driven field we will employ later on to provide a robust, flexible, and re-usable run-time evaluation engine for the UCON policies. But before diving into the following subsections, it is worth to remark that the word model has slightly different semantics in the field of access control and the field of model-driven engineering. While in access control, a model is a formal definition of a set of access control rules that is technology-independent of how these rules are later enforced, a model in model-driven engineering is an abstract representation of a system perspective that conforms to a specific language/metamodel  [15].
Policy definition In order to allow the definition of UCON access control policies, we have created a Domain-Specific Language (DSL) that captures and organizes the UCON concepts as defined in  [16]. In the following, we describe the abstract syntax of this DSL which we have implemented as an EMF metamodel 3   [17]. Note that as concrete syntax we will use off-the-shelf tree-based model editors and XMI serialization, and that execution semantics are provided by translating the access-control policies to executable model-transformations specifications as we will show later. Alternative concrete syntaxes (either textual or graphical) can be easily defined with other EMF-based tools like Xtext 4  or Sirius,  5 highlighting once again the side benefits we get once we enable UCON to the plethora of EMF modeling tools through the definition of our DSL.
Well-formedness rules Models created with our UCON metamodel will be all syntactically correct. However, not all will be semantically correct. This is so because the language used to create the metamodel is not expressive enough to specify all the details and/or because doing so would make the metamodel more complex and less usable. In order to solve this issue, we need to provide well-formedness (WFRs) rules to validate the model. We use OCL for this purpose as aforementioned. Note that OCL integrates seamlessly with (some) existing MDE tools and permits the automatic evaluation of these rules at design time in order to produce correct UCON policies. That is, once the WFRs for UCON are defined, state-of-the-art OCL tools can be used to automatically ensure designers only specify correct UCON models.