dataesr/scientific-paragraphs-categorization huggingface dataset:
20 urther research should continue to scrutinize the factors driving specific deployments as well as the differences we find between democracies and non-democracies. The popularity of the military, or its historical role within the country, is likely to influence deployments  (Heinecken, 2021; Levy, 2021). 21 he global COVID-19 pandemic is a unique opportunity to observe how various governments respond militarily in crisis situations. We hope that our dataset is a worthwhile contribution to scholars attempting to understand military operations in peacetime and domestic military deployments. We additionally find support for recent work theorizing that civilian political officials use the military in ways that bolster their own political image and chances of electoral survival  (Kenwick and Maxey, 2022).
Acknowledgements. We thank  Chiara Tulp  for her excellent research assistance during the data collection,  Nguyen Ha  for her help in the initial data cleaning phase and the editors and anonymous reviewers for their helpful and constructive feedback. All remaining errors are our own. We gratefully acknowledge financial support from the  Swedish Research Council  (grant number  2017-02139 ) that made the data collection possible and the  Royal Swedish Academy in Letters , History and Antiquities.
dowjones. com/professional/factiva/. 8These are available upon request. 9 All Africa, 2020, 'Sudan: Military Employed to Enforce Anti-Covid-19 Measures'. 1 May. Available at: https://allafrica. com/stories/202005040361. html. 10 See https://www. v-dem. net/documents/14/dr_2020_dqumD5e. pdf. 11 North Korea is the exception; it is the only country in our dataset with missing data. 12.
g. , https://www. rtl. hr/vijesti/hrvatska/saznalismo-tko-je-misteriozni-fred-i-tko-stoji-iza-popularne-aplikacijekoju-su-hrvati-srusli-5639294c-b9f3-11ec-8db4-0242ac120035, last accessed 8 July 2022. In Croatian language). @LastQuake allows users to ask questions and some general questions appear repeatedly, such as the cause of magnitude discrepancies between agencies. Others, based on their personal experiences, challenged the very possibility for a given aftershock to have been felt, or questioned the magnitude estimate, a misunderstanding due to the frequent confusion between intensity and magnitude. This highlighted the need for educational messages on seismology and the way the LastQuake system operates. In addition, the LastQuake system has also detected some nonseismic events. This was the case in 2017 when the online reaction of the public proved to be related to an earthquake prediction that did not materialize in Punjab India  (Martin et al.
, 2021). In other cases, the cause can be identified by Twitter users themselves, such as in the cases of sonic booms and of a meteor's atmospheric entrance on 20 February 2020 over the region of Zagreb (Croatia). Finally, in practice, @LastQuake is the place to communicate with the public about the different components of the LastQuake system (Figure  1 ). A seismic activity grid pattern was observed on an interactive seismicity map of La Palma (Canary Island) during the 2021 eruption of the Cumbre Vieja volcano, occurring due to the rounding up of earthquake location coordinates; in a related article,  Fallou et al. (2022b)  present how the grid pattern was exploited in support of conspiracy theories and how EMSC attempted to debunk them on Twitter. There are also far more positive usages; Twitter has been used to identify volunteers for translating the LastQuake app, which is now available in 42 languages due to their contributions.
Moderation policy for @LastQuake The @LastQuake moderation policy was developed from experiences faced over time. It is applied to any tweet containing our Twitter handle. This can be an interaction with one of our own tweets, a direct question, or an attempt to benefit from our large followership (e. g. , for advertising purposes). The policy aims to maximize the reliability and credibility of our timeline and avoid the exposure of inappropriate messages or content via @LastQuake. Inflammatory, insulting, and offensive language is banned, as well as spam, advertisements, proselytism, and any type of discrimination or political statements. More specifically to @LastQuake, we refuse the association of our timeline with any non-scientifically based claims, notably earthquake predictions. This moderation has been implemented by asking for the deletion of the tweet by its author and/or by blocking the account.
Using the Twitter API (application programming interface), tweets containing the keyword "earthquake" in the local language and published after the occurrence of a felt event are automatically detected. An automatic reply is then published, inviting its author to share her/his observations using the LastQuake app or website to help document the earthquake's effects. To avoid spamming, especially during aftershock sequences, the same user cannot receive more than one invitation every 6 months. Finally, after destructive earthquakes or a sequence of earthquakes, contact with the national seismological institutes is established to avoid possible unwanted hindrances to their own communication. Such contacts were established in Albania, Croatia, and very recently in Cyprus; in these three cases, the national and EMSC's activities on social media proved to be synergetic.
Acknowledgments The authors thank  Matthieu Landès ,  Robert Steed , and  Guillaume Ucciani  for their direct contributions to this study as well as the other EMSC team members, without whom the LastQuake system would not exist, and  Robert Steed  for improving the English. They also express their thanks to the PetaJakarta project for the valuable exchanges. They extend their thanks to the reviewers who helped in improving the quality of the manuscript. This study is about Twitter, not by Twitter; the terms "Twitter" and "Tweet" are the trademarks of Twitter, Inc. or its affiliates.
Methods Comprehensive electronic literature searches were conducted using Emerald Publishing, Science Direct and Taylor and Francis databases to look for relevant papers systematically. The journals housed within the Emerald Publishing, Science Direct and Taylor and Francis databases analysed in this review article are peer-reviewed. Therefore, this implies that the papers have been thoroughly checked and approved by experts for the journals before publishing. This qualitative analysis used 54 journals and 4 documents (policies, special reports and strategies) published between 2001 and 2021. The topic and, in some cases, the abstracts of the papers were read to scrutinise the relevance of the papers to the study and review article. This enabled the researchers to sieve out useful papers from other papers found to be irrelevant.
The Scientific Revolution is one of the most important phenomena in human history. Yet it is ill understood, partly because of a lack of quantification. Here, we leverage large datasets of individual biographies to build national estimates of scientific production during the early modern period. While aggregate levels of national production are unsurprising, per capita estimates reveal striking differences across countries, with the two richest countries of the time (England and the United Provinces) being much more scientifically productive than the rest of Europe. Overall, our study demonstrates a strong association between scientific creativity and per capita income. We also show that scientific creativity is associated with other kinds of creative activities in philosophy, literature, music and the arts, suggesting a common underlying factor.
However, testing these theories with only qualitative data is difficult. So far "there is no general agreement on what the Scientific Revolution is, why it happened -or even whether there was such a thing"  (Wootton, 2015). However, thanks to the growing size of online datasets, it is now possible to quantifying cultural production over time in science as well as in the arts  (Fraiberger et al. , 2018 , Gergaud et al. , 2017 , Schich et al. , 2014 , Serafinelli and Tabellini, 2017 , Sinatra et al. , 2016). In this paper, we use the biographies of Wikipedia to quantify scientific production. In doing Table  1. Scientists ranked by the importance of their contributions to the advancement of science so we include all individuals who are classified as scientists in Wikipedia during the early modern period: mathematicians, astronomers, geographers, physicists, biologists, naturalists, chemists, botanists, entomologists, and zoologists.
The use of large datasets like this one make it possible to go beyond key figures such as Newton and Galileo and take into account the thousands of individuals who contributed to the rise of science (see Table  1 ). In the same way, it allows us to incorporate not only the discovery of the law of gravitation or the moons of Jupiter, but also the hundreds of mathematical theorems and astronomical discoveries that paved the way for the major breakthroughs of the Scientific Revolution. Importantly for science, datasets like Wikipedia also convey an implicit estimate of the significance of a cultural product: the more numerous the productions of an individual, and the more important they were in the light of modern people, the bigger the biography. As such these estimates are very similar to the modern bibliometric assessment of scientific productivity, because the value of a scientific contribution is estimated by the later generations of scientists and editors of Wikipedia (see Table  1 ).
A further advantage of Wikipedia is that it is edited by thousands of editors, rather than by a single author or a small team of authors. For instance, the page for Galileo has been edited by 2931 different editors (excluding bots) since its creation in 2001. Even the pages of minor figures such as   Johnson (1595 -1644, English botanist)  or Joachim Jungius (1597-1657, German mathematician) have been edited by more than 30 different contributors. This guarantees that the database has not been built with a particular hypothesis in mind. In fact, many methodological decisions were made by the editors of Wikipedia. For instance, for each individual, Wikipedia provides one or several "occupations"  (writer, painter, theologian, etc. ). This label allows us to distinguish between individuals who truly contributed to science (e.
5  and 6 ). Another potential bias is due to the fact that England and the Netherlands are smaller than Italy, France and Germany. It could thus be the case that their high productivity was due to the fact that similarly highly productive areas in France (such as Ile-de-France) or Italy (such as Tuscany) were hidden within bigger areas. To account for this possibility, we because of their scientific contributions (although these are marginal in regard to their non-scientific contributions). It could thus be the case that the results were biased by these non-scientific celebrities. In an additional analysis, we only included individuals whose primary occupation as listed on their Wikidata item page was as a scientist (in one of the categories specified above). This did not substantially change the pattern (see Fig.
Second, we added the date as a covariate to (1) control for a potential historiographic bias, overstating the scientific production in the latest studied period because of the abundance of sources, and (2) to avoid a spurious correlation between affluence and scientific production, due to the fact that both affluence and scientific production tend to increase with time. As random effects, we added the country (to account for the fact that the data points associated to the same country are not fully independent), and the method used to measure scientific production (the Wikipedia language and the proxy (bytes, languages or links). To avoid any spurious result due to autoregressive components, heteroskedasticity or non-normality of residuals, we used robust regressions provided by the R package robustlmm.
rproject. org/web/packages/strucchange/strucchange. pdf]. It follows the methodology of the Chow test: for each date, the "Chow statistic" is computed, following this formula: F = S c -(S 1 +S 2 ) (S 1 +S 2 )/(n-4) (S c being the residual sum of squares for the initial model, S 1 and S 2 the ones for the model fitted respectively on the first and second group of observations, and n the total number of observations) Intuitively, the statistic represents how much the fit is improved if, instead of fitting an OLS regression for the whole dataset, we fit one for the points anterior to this date and one for the posterior ones. Under the null hypothesis (i. e. the two regression lines have the same intercept and the same slope), F/2 follows a Fisher distribution with 2 and n -4 degrees of freedom.
In fact, a growing body of works both in political science  (Abramson and Boix, 2014, Boix and Stokes, 2003)  and in the behavioral sciences  (Petersen and Aarøe, 2015, Safra et al. , 2017)  suggests that better life conditions are associated with higher level of interpersonal trust and higher support for inclusive institutions. In other words, institutional development, political freedom and inclusiveness are endogenous, not exogeneous, and they accompany economic development  (Boix and Stokes, 2003). In any case, future work should use causal or quasi-causal methods and find variables in datasets that are assigned in a way that is as good as random, as in the case, for example, of the distance from the coast in the study of the impact of the Atlantic slave trade  (Nunn and Qian, 2011) , or soil composition when studying the impact of the introduction of the heavy plow  (Andersen et al.
Methods Scientific and Artistic Production. To collect the datasets, we used a top-down approach based on Wikipedia categories  (Gergaud et al. , 2017). First, we manually identified all the relevant Wikipedia categories (for instance, "18th century Italian mathematicians"). We only included individuals working in the "hard sciences": mathematicians, physicists, astronomers, chemists, biologists, botanists, and zoologists (the first three categories account for more 90% of the sample). The rationale for this choice is that these domains correspond to the core domains of the 17th century Scientific Revolution. It also corresponds to what economists call "useful knowledge," the kind of knowledge involved in the manipulation of the material world, and the main source of growth during the Industrial Revolution  (Jacob, 1997; Mokyr, 2016).
Some extra creative occupations could have been included, but were excluded due to the small number of individuals in each category: engineers, geographers, explorers, cartographers, or architects. In total, 22,943 individuals were included in the dataset (see  We included in our sample all modern countries for which there were lists by nationality in science for the 19th century or before. Our final list includes the following countries: Belgium, UK, France, Germanic countries (Germany, Austria, Switzerland), Italy, the Netherlands, Iberia (Spain and Portugal), Poland, Russia and Scandinavia (see Fig. 15 ). We created two aggregated countries (Germanic countries and Iberia) because some environmental variables were only available at this level (see below, urbanization). Where country of citizenship (around 30% of the pages) was not specified on the individual's Wikidata item page, we inferred it using two methods.
First, when possible, we referred to the Wikipedia category in which we had found the page (e. g. , someone belonging to the category of "Italian mathematicians" was considered Italian). Second, when this was not possible, a script looked at the first words on the page, more precisely those following the pattern "[Name] ([dates]) was a. ". Therefore, a page beginning with "Galileo Galilei (15 February 1564 -8 January 1642) was an Italian astronomer" meant that Galileo would be classified as Italian. Finally, for the most incomplete datasets (Renaissance scientists, Renaissance philosophers), we added country information manually. We collected three proxies for the importance of an individual's contribution to the advancement of science  (Gergaud et al. , 2017) : 1. Length: The number of bytes in the Wikipedia page (from "Page Information") 2.
Languages: The number of languages in which there was a page for the individual (from Wikidata) 3. Quotation: The number of Wikipedia pages containing a link toward this page (from the corresponding Wikidata item page). None of these indexes is perfect, and they all have their biases, so we calculated z-scores for each of them, and combined the three to create a more general index of Individual Scientific Productivity (for scientists) or Artistic Indivividual Productivity (for artists)(see Fig. 4 ). To avoid a potential language bias leading to an overestimation of British production, we ran this study on the three largest Wikipedias: English, French and German. Contrary to our expectations, the datasets from the French and German Wikipedias were not always smaller than those from English Wikipedia (for instance, the German-language dataset of scientists is much larger than the English-language dataset, while the opposite is true for painters).
Such an electoral system generates a large discontinuity between vote shares and the share of seats in the municipal council. For example, a list just above 50% of votes will get 75% of the seats. As a result, the list that obtained the most votes is guaranteed to govern the municipality, as no post-electoral coalitions among other lists could reach a majority and overturn the results. We exploit this feature in our RDD framework. We use electoral data collected by the Center of Socio-Political Data of Sciences Po from electoral authorities. We exploit five elections that occurred in  1983, 1995, 2001, and 2008  in municipalities with more than 9 000 inhabitants in the election year. 6 Even if their administrative organization is different, the 20 districts ('arrondissements') of Paris and the eight and seven electoral sectors of Marseilles and Lyon are also included in the sample and considered as separate municipalities.
nicipalities. Similarly, 22. 2% of inhabitants lived in public housing in these municipalities against only 8. 6% in other municipalities. We categorize the lists in the final election round as either left-or right-wing using the classifications of French electoral authorities. Most of the time, this assignment is straightforward, as there were no national or local bipartisan coalitions over the period. 8   Table  1  summarizes the characteristics of municipal elections in our sample. While the sample is unbalanced as the electoral data was only collected for municipalities with a population above 9 000 inhabitants, the number of municipalities is quite similar across the elections, ranging from 608 to 805. Column 2 reveals that a second round occurs in 40 to 60% of elections, except in 1983 when electoral competition mainly involved two competing coalitions  (Dupoirier et al.
9 As is the case for most local authorities, mayors cannot use gerrymandering to change the border of municipalities. To mitigate the possibility that differences in household size might influence our measures, we use the head-of-household as the unit of analysis. We estimate all characteristics of municipalities, such as the share of immigrants or public housing inhabitants, using the head-of-household level. 10 Information on the country of birth and citizenship, distinguishing between native and naturalized citizens, is reported in the Census. However, there is no information on the ethnicity of descendants of immigrants. The Census data allow us to identify immigrants who can vote in local elections. This group includes immigrants with French citizenship and, since 1992, all immigrants from the European Union.
Municipal policies We focus on two main measures of municipal policies that can be observed in available data sets. Taxes -We use data from the Recensement des éléments d'imposition, an administrative database collected by the French Ministry of Finance that contains the total amount collected and the tax base for each municipality and tax from 1982 to 2014. A limitation of this dataset is that it does not contain any information on how the budget is spent. We assess whether elections affect the rate of the two main municipal taxes levied on households: the housing tax and the property tax, both based on net rental value. Consistent with  Charlot et al. (2008) , Panel C in Table  2  shows that these two tax rates increased regularly over the period. Public housing -The second crucial local policy that we examine concerns municipal public housing.
Robustness Other samples and specifications -We document in Table  12  the robustness of our main results using alternatively as a dependent variable the growth of immigrant-headed households in Panels A1 and B1 and of households in public housing in Panels A2 and B2. First, we assess in Panel A the sensitivity of our estimates to alternative samples. In Column 2, we include in our sample the 1989 election, which was not included previously as the Census data do not allow us to observe the outcomes 6 years after this election, unlike other elections. In Column 3, we include municipalities in smaller urban areas with less than 30 000 inhabitants where few immigrants live and which contain little public housing. In Column 4, we exclude the districts of Paris, Lyon, and Marseilles, which are not fiscally independent from the central municipality.
In Column 5, we exclude elections in which a regionalist or independent list, which we somewhat arbitrarily classified as right-wing, competed in the final round. In Column 6, we exclude the 5% smallest and 5% largest municipalities. In Column 7, we exclude municipalities that are not observed at each election in our baseline sample. Overall, the results are in line with previous estimates, albeit more imprecise with the 1989 election and quantitatively lower when smaller urban areas are included in the sample. Panel B of Table  12  investigates the robustness of our results to alternative construction of the data or specifications of the statistical model. In column 1, instead of constructing our dependent variables at the household level, the number of immigrants and inhabitants in public housing is measured using the population of all adults of more than 18 years of age.
This research was supported by grants from the  Agence Nationale de la Recherche  ( ANR-10-EQPX-17 ,  Centre d'accès sécurisé aux données ,  CASD ,  ANR-11-IDEX-0003-02/ Labex ECODEC  No. ANR-11-LABEX-0047 ), the  "Flash Asile" program  ( ANR-16-FASI-0001 ) and  Université Paris-Saclay  ( ANR-11-IDEX-0003-02 ).
The selection of the interviewees diversified the activists' profiles by age, date of arrival in the movement, gender, level of politicization prior to the movement, political orientation and geographical location. We recruited activists through the Meetup platform and used a snowball method until sufficient diversification of profiles, based on existing literature  (Biorcio, 2015; Ieraci and Toffoletto, 2018) , was achieved. All this information is stored in a database used to characterize and interpret interviews. Among the interviewees (see the Appendix), 18 are currently active in the movement, 7 are or were elected at the local level and 21 have left the movement (after 2018, except for one). On average, they have been involved in the M5S for 9. 4 years. In total, 46% became involved before 2013, and the other afterwards.