dataesr/scientific-paragraphs-categorization huggingface dataset:
Laughter attitudes were also reported. Finally, the perceived basic emotions -joy, anger, fear, sadness, disgust,  (Ekman, 2016 ) -were associated with positive or negative expression. We give some examples of how emotional behaviors and basic emotions are coded: laughing is coded as a visual and auditive manifestation; smiling is coded as a visual manifestation only; joy is expressed inside the interaction, with multiple body gestures and is related to the emotional context of the relationship. Joy implies behaviors that are more complex that smiling and laughing only. To have an in-depth exploration of the interactive processes in adult-child relationship, THEME6EDU© software was chosen because it is able to detect interaction patterns invisible to the naked eye: the algorithm it employs recognizes T-patterns, that is, patterns that emerge from the analysis of behaviors/events over time and level by level and, in our case, specifies that the fractals should include feedback loops characterized by symmetric translation  (Magnusson, 2017).
The T-Pattern analysis "has allowed the description and detection of intra-and inter-individual causal and non-causal patterns frequently sharing the T-pattern structure, but the detection of intra-individual patterns may be a precondition for the detection of more complex inter-individual patterns"  (Magnusson, 2020). For this microanalysis a 2-min interaction deemed particularly representative of the adult-child relationship, was selected from all the recordings of the child's interactions with caregivers; a similar sequence occurred repeatedly with a significance in the THEME© software where p < 0. 005. The choice of sequence was also guided by an analysis of clinical elements. Video analysis was performed in order to identify interactional patterns that were replicating within relationships.
The adult reacts to the child's ERS and with feedback the child will respond to this adult input and will adjust his or her own ERS. The video microanalyses provided essential information on the deep structure of interactions and allowed us to identify a network of interacting loops (Simon-Herrera and Duriez, 2021). Only the most complex and heuristic patterns are presented. The THEME© software also provides information about occurrences of behaviors and ERS as well as percent values of interaction initiated by Julia with her different caregivers. To ensure the validity of the data, the Monte Carlo test was performed on THEME©. This test consists of comparing the number and complexity of the real data versus the randomized data to obtain statistical validation. Debriefing and video-feedback with the participants ensured that the coding matched the emotional intent expressed by the adults.
Occurrence of behaviors by type of interaction Table  2  shows the occurrence of ERS by adult and by child in the different interactions. Those occurences are directly generated by the THEME software and give a first look at the most used ERS in each type of interaction. Figure  1  shows how often Julia initiated the interaction with her caregivers. We can see that the percentage of interaction initiation of Julia is superior with her mother rather than when she is with, her foster mother or her father. It indicates that 68% of the interactions between Julia and her foster carer were initiated by Julia. Her behaviors were focused on engagement with an object while the foster carer spoke with her in a calm voice, employing cognitive reassessment; Julia similarly showed a preference for positive verbalizations and language elaboration.
Interaction patterns analysis Analysis of interaction patterns, derived from analyses performed with the THEME© software, highlights the processes and behavioral chains that are established during exchanges between the child and each of her reference adults. Figure  2  shows the patterns of interaction between Julia and her foster carer. Together they performed 129 acts. It shows that when Julia was engaged with an object (e. g. , painting, playing with a doll), her foster carer spoke in a calm voice, prompting cognitive reappraisal and reinforcing Julia's object engagement. This in turn led to positive verbalization and mental elaboration by both Julia and her foster carer, who continued the exchange in a conversational mode so long as Julia remained engaged with an object. The blue lines indicate the self-excitation behaviors that occurred, mainly during pauses in this pattern.
, 2022). It is complicated for a foster child such as Julia to develop ERS within multiple parenting systems. It is important to consider as a limitation the fact that a single-case study is presented and the results cannot be globalized. However, the methodology detailed here offered the opportunity to analyze in-depth interaction processes in foster care. The microanalysis carried out with the THEME© software clearly highlights these mechanisms and their disparities according to the type of adult-child relationship, making it an invaluable tool in the search for relational patterns in clinical psychology. These patterns must be understood as interactive loops that are reproduced throughout the exchange  (Simon-Herrera et al. , 2022). While disengagement and self-excitement behaviors seemed to be particularly present in the interaction between Julia and her caregivers, no specific pattern showed the process explaining the use of those specific strategies.
Data sampling Our empirical data comprise interviews, internal documents and communication materials, and meetings with nascent international entrepreneurs and managers (French entrepreneurs entering the Chinese market), international support providers, and other actors deemed relevant by interviewees. Respondents were identified through a mixed positional (based on status and function), purposeful  (Patton, 2014) , and reputational (identified by other respondents), snowball sampling  (Denzin and Lincoln, 2012)  approach. We initially identified five interviewees from previous contacts we had in China and from the list of associations established by the French Chamber of Commerce and Industry in China and the French Ministry of Foreign Affairs. At the end of each interview, we invited respondents to name persons deemed relevant for business development in China.
We also collected additional documentscompany reports, communication brochures, internal documents, companies' or associations' newsletters, books published by participants or support providers, etc. to triangulate the data and improve the internal research validity  (Denzin & Lincoln, 2012). These documents provided interesting information on the companies' internationalization process but also their general understanding of specific issues, their relations to other actors in the field, their perceptions of China and Chinese people, and so forth. The research and data collection phases are described in Appendix B. Interviews and additional documents were fully recorded, transcribed, coded, and analyzed. Over 1500 pages of interview transcripts, 300 pages of observations, and another 300 pages of secondary data were thereby collected and analyzed.
Data analysis We used NVivo qualitative data analysis software to code all interviews as well as documents and observations. Analyses were performed in two steps  (Gioia, Corley, & Hamilton, 2013). During the first round of coding (1st-order analysis), we assigned descriptive codes to all text passages that evoked activities and processes occurring before, during, or related to the internationalization endeavors. The initial coding was performed according to the broad categories covered in the interview guide and progressively enriched with informants' own terms: personal and professional background and experience, creation and use of social networks for international development; reactions of people met during the process, meaning given to these reactions; results and performance. A review and consolidation of these descriptive codes yielded an initial scheme with approximately 70 codes ("nodes" in NVivo's terms) that facilitated the comparison of topics across data sources.
Data and Descriptive Statistics We use individual-level survey data from the Employment and Unemployment Survey (EUS) by the National Sample Survey Office (NSSO) of India, rounds 60 (collected from January 2004 to June 2004), 62 (collected from July 2005 to June 2006), 64 (collected from July 2007 to June 2008), 66 (collected from July 2009 to June 2010), and 68 (latest available, collected from July 2011 to June 2012). Because round 60 was only collected during 6 instead of 12 months, we will check the sensitivity of our results with respect to exclusion or inclusion of round 60. Round 61 is excluded because our estimating equation will contain a lag structure and we want to maintain a similar (2-year) lag throughout the sample. Using sampling weights, we build regional-level data (at the state/union territory or district level) for the population growth factor, the non-employment rate (1 minus the employment-population ratio), and the unemployment rate.
Acknowledgments We thank  Himanshu ,  Balwant Mehta ,  Priyanka Tyagi , employees of the  National Sample Survey Office  (NSSO) and participants at the 2022 Annual Conference of the Indian Society of Labour Economists for helpful comments. Both authors declare that they have no conflicts of interest. The project has not been funded by resources external to our own university. A previous version of this article has been issued as a discussion paper, see Braschke and Puhani (2022), for example. Funding Open Access funding enabled and organized by  Projekt DEAL. No funding was received to assist with the preparation of this manuscript.
14. Just like Expect_ contr, Unpaid_contr is lagged one period. The expected signs on these variables reflect the idea that lobbying is exerted only for marginal sites, namely, those of lower quality. Hence, 12 We have constructed the dataset for Committee referring to official data available from the UNESCO website about each Committee Assembly. 13 Data for Expect_contr are drawn from the UNESCO Statements of Compulsory and Voluntary Contribution to World Heritage Fund. They are expressed in US dollars for each country in PPP. 14 These data come from official documents of UNESCO (Statements of Compulsory and Voluntary Contribution to World Heritage Fund). In the case of countries that provide only voluntary contributions, Unpaid_contr is set equal to 0 (unless they have not contributed at all).
ACCEPTED MANUSCRIPT -CLEAN COPY [Table  8  about here] 6. 4. Specification of the model. As a final robustness check we have estimated a crosssection model to exclude the possibility that our results depend on the model specification. Given the different structure of the dataset, we are obliged to estimate a second equation: 𝑄 𝑖 = 𝛽 0 + 𝛽 1 𝑁 𝑖 + 𝛽 2 𝐶𝑢𝑙𝑡𝑢𝑟𝑎𝑙𝐾 𝑖 + 𝛽 3 𝐿𝑜𝑏𝑏𝑦 𝑖 + 𝛽 4 𝐴𝑉𝑞𝑢𝑎𝑙𝑖𝑡𝑦 𝑖 + 𝑢 𝑖 (2) The dependent variable Qi is the quality of a single site i whose value, just like in the analysis conducted so far, equals the number of criteria that the site satisfies. Having removed from the sample the sites excluded from the WHL, whose values would have been zero, Qi is a positive integer with a lower bound equal to 1. Like in the estimates of equation (  1 ), we proxy the number of sites N, by Sitest-1 and Tenure; yet the cross section specification allows us also to include Year, i.
The cross-sectional specification of equation (  2 ) has several additional advantages. F irst, it allows to keep more than one observation for every year, without the need to compute ACCEPTED MANUSCRIPT -CLEAN COPY country averages of the quality of the sites. Second, it rules out the problem of the missing values. Third, as the sample contains only positive integers, we can test our hypothesis using a count data model. Since Q is not over-dispersed and its mean and variance show quite similar values, we assume a Poisson distribution. 18 The descriptive statistics related to the cross-section dataset are shown in table  11. Table  9  shows that changing the specification of the model from a panel to a crosssection, where single sites rather than country averages are considered, does not qualitatively change the results.
Third, traffic offenses are very homogenous, unspecific, and often victimless, making caseheterogeneity less of a concern as a potential confounder 1. In practice, French magistrates often make decisions relying on the same set of basic information as available in our dataset (socio-demographic information about offenders, type of offense, alcohol intake and criminal background). Fourth, the majority of traffic offenders are easily identified during police stops and guilt is often implied. As a consequence, investigation costs are usually close to zero and lead to a very high prosecution rate, which means a low sample selection of cases. Fifth, traffic offenses receive highly standardized judicial treatment using rules and guidelines that are often very explicit, although not made public. This usually entails a limited number of criminal procedures and sanctions.
There is substantial heterogeneity between courts. At the extensive margin, the indirect effect is negative and very low in three courts, but the court effect is never fully explained by the decisions made by prosecutors. We conclude that prosecutors have a rather limited independent impact on disparities when choosing between criminal procedures. The remainder of our paper is organized as follows. Section 2 reviews the existing literature on sentencing disparities along with their underlying explanations. Section 3 presents the institutional context of traffic offenses in France and Section 4 describes the dataset. In Section 5, we study differences in sentences between courts both at the extensive and intensive margins. In Section 6, we investigate the role of prosecutors when explaining the courts' disparities.
Data and descriptive statistics We study differences in sentences across courts using an original dataset collected from seven courts located in the South-East of France. In each court, the database focuses on all traffic offenses that were prosecuted (not dismissed) during the six-month period from January 1 st to June 30 th , 2017. This includes offenses like driving under the influence of alcohol or drugs, driving license offenses, or offenses related to the lack of car insurance. The seven sampled courts represent a small fraction of the 163 first-instance courts in France. Indeed, access to data is very difficult to obtain and most often not allowed. In our context, the data were collected in the courts by field experts (see  Joseph-Ratineau, 2019). Data collection was very costly through manually collecting paper files and hand-coding decisions.
The largest court (TGI4) will later serve as the reference category in our empirical analyses. TGI4 is characterized by high unemployment and high crime rate. Courts with below average population tend to experience lower unemployment and lower crime rates. These differences in local contexts may partly explain judges and prosecutors' decisions when handling cases, although we observe too little courts to properly run statistical tests. The dataset includes three main types of variables. First, we have some offender-level characteristics: gender, age (in categories), occupational status and existence of past convictions which trigger legal recidivism. Second, we know the type of offense committed and the alcohol intake if any (usually controlled during police stops). Third, we know the type of procedure chosen by the prosecutor as well as the type of sentence and the associated quantum.
2%), 588 in TGI6 (15. 3%) and 202 in TGI7 (5. 2%). When explaining court disparities, judges may have an influence on the severity of the sentence. As there were the initials of the first name and last name of each judge in the database, we decided to collect additional data on judges using public records from the Journal Officiel de la République Française. This includes information on gender, date of birth, year of entry as judge and year of entry in the current court, from which we deduce age, years of experience and court-specific years of experience. We were able to obtain those additional characteristics on judges for 3,395 offenses, corresponding to a matching rate of 88. 1%. The contributions of courts to the final sample are 9. 5% (TGI1), 9. 4% (TGI2), 7. 0% (TGI3), 38. 8% (TGI4), 16.
Concluding comments The purpose of our paper was to contribute to the existing literature on differences in sentences between courts, but with a fresh angle related to the influence of prosecutors on such disparities. For that purpose, we have used unique data on traffic offenses from a sample of seven courts located in South-East France. We complement this dataset with some individual characteristics of judges. The situation is different for prosecutors. As a given case is processed by different prosecutors throughout the legal procedure in French courts, we focus on the type of procedure (simplified versus classical) chosen by prosecutors. We reach two main conclusions. First, there are sizeable disparities in sentencing between neighboring courts handling highly homogenous cases of traffic offending.
Also, this rules out the possibility of working on prosecutor-judge dyads as done in  Kim et al. (2015). In our framework, the prosecutor's heterogeneity is picked up in the type of procedure (classical versus simplified). Third, we investigate cases handled by seven neighboring courts in 2017. Given this proximity, one could expect more similar decisions, meaning that our estimation of court disparities is presumably a lower bound of the overall disparities that would be observed with a sample of all French courts. At the same time, having only a few courts precludes any attempt to correlate the courts effects with some local indicators like composition of the population, crime rate or available beds in prisons. Production of exhaustive data on all criminal procedures in all courts by the Ministry of Justice would be very welcome to further investigate and understand differences in sentence decisions between courts in France.
Although no EU country has been directly attacked, the threat of what has been qualified as a 'civilizational' war, that is, a war of individual and social values, remains quite serious. In this recent and unusual geopolitical context, we employ data from our own online survey to explore empirically whether the Russian aggression against Ukraine has induced a shift in attitudes towards enhanced European integration. Our survey was conducted amongst students of various European universities in early 2022. The survey happened to be launched shortly before 24 February 2022 and remained open until mid-March 2022. This particular timing allows us to analyse in a quasi-experimental framework whether the news about the Russian aggression, ceteris paribus, affected participants' responses to the various questions in the survey.
Survey Organization and Design The data used in this study were collected as part of the European Students Mobility Experience Survey (EUSMES). The original objective of this survey was to investigate the impact of participating in a semester abroad on students' attitudes and sense of identity. The survey took place in two rounds: the first round in May-June 2021 and the second round in February-March 2022. 3  The second round of the survey was launched on 21 February 2022. On or shortly after that date, administrations of participating universities sent out an email to students who had just completed an Erasmus stay, inviting them to participate in the survey. For instance, JGU Mainz (Germany) sent out the invitation on 21 February, TU Darmstadt (Germany), University of Lille (France), and WU Vienna (Austria) on 22 February, KU Leuven (Belgium) on 23 February and GU Frankfurt (Germany) on 25 February.
Acknowledgements We are grateful to the administrators at participating universities for their support and for distributing the survey questionnaire to their Erasmus students. We thank  Maximilian Herbert  for excellent research assistance. We benefited greatly from comments and suggestions received from  Micael Castanheira ,  Jan Fałkowski ,  Stefania Lovo , and conference and seminar participants at  Bofit ,  ECARES ,  HWR Berlin ,  JGU Mainz ,  Reading University ,  University of Warsaw ,  Silvaplana Workshop  in Political Economy,  Slovak Economics Association Meeting , and the  CESifo Workshop on Political Economy. We are indebted to three anonymous referees and to  Paul James Cardwell  as the editor in charge of our article. Piotr Stanek  gratefully acknowledges financial support granted to the  Cracow University of Economics  under Project No.
Present and future FWI were estimated from weather data derived from 13 climate models of the EURO-CORDEX initiative for RCPs 4. 5 and 8. 5, which were developed for IPCC Assessment Reports and representative of the currently most-likely radiative forcing trajectories. For each of the 26 FWI simulations, Firelihood was used repeatedly by sampling from the posterior distribution of the Bayesian model to provide 300 replications of fire activity, which represent both the distributional uncertainty associated with stochastic processes and the statistical uncertainty from the estimation of model parameters. Such replications allow to compute fire occurrences and sizes in each pixel × day through different fire metrics of numbers of fire larger than certain thresholds (N1ha, N10ha, N100ha, N1000ha) and burnt areas (BA).
Robbeets et al. 1  argue that the dispersal of the so-called "Transeurasian" languages, a highly disputed language superfamily comprising the Turkic, Mongolian, Tungusic, Koreanic, and Japonic language families, was driven by Neolithic farmers in the West Liao River region of China. They adduce evidence from linguistics, archaeology, and genetics to support their claim. An admirable feature of the Robbeets et al. 's paper is that all their datasets can be accessed. However, a closer investigation of all three types of evidence reveals fundamental problems with each of them. Robbeets et al. 's analysis of the linguistic data does not conform to the minimal standards required by traditional scholarship in historical linguistics and contradicts their own stated sound correspondence principles.
Acknowledgements The work was funded by the  Department of Linguistic and Cultural Evolution ,  Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany , the  National Natural Science Foundation of China  ( 32070576 ,  32070577 ,  91731303 ,  31771325 ,  31801040 , and  T2122007 ), the  National Key R&D Program of China  ( 2020YFC1521607 ), the  Major Project of National Social Science Foundation of China  ( 21&ZD285 ,  20&ZD248 , and  20&ZD212 ), the  "Double First-Class University Plan" key construction project of Xiamen University  (the origin and evolution of East Asian populations and the spread of Chinese civilization,  0310/X2106027 ),  Nanqiang Outstanding Young Talents Program of Xiamen University  ( X2123302 ), the  Major Project of National Social Science Foundation of China  ( 2021MZD014 ), the  European Research Council (ERC)  grant to  Dan Xu  ( ERC-2019-ADG-883700-TRAM ), the " Shuguang Program " supported by  Shanghai Education Development Foundation  and  Shanghai Municipal Education Commission  ( 20SG06 ), the  Scientific and Technology Committee of Shanghai Municipality  ( 18490750300 ), the  National Key Research and Development Program  ( 2020YFE0201600 ),  Shanghai Municipal Science and Technology Major Project  ( 2017SHZDZX01 ), the 111 Project ( B13016 ), and the  China Postdoctoral Science Foundation  ( 2021M691879 ,  2021M691882 ).