dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, participants provided demographic information, including age, gender and education level (which are included as control variables in all models reported here). For other questions asked in the survey as part of the larger project on the normative and social aspects of COVID-19, see  Tuncgenc et al. (2021). Open practices statement. A full demonstration of the survey can be found at the Open Science Foundation (OSF) repository for the broader project (https://osf. io/ke5yn/). The OSF repository for this specific study (https://osf. io/s5mdh/) contains the data and analyses. The survey design was preregistered at the above project repository. The same registration included the hypothesis that adherence to official guidelines would be predicted by trust in science. For other hypotheses in the broader project (not relating to trust, see  Tuncgenc et al.
Results Overview of sample. Of the 6675 participants who finished the survey, 1577 opted out of the question on political ideology and 1199 indicated that they had no close circle (in the specific sense of 'close circle' as operationalised here: see the "Methods" section). This leaves 4341 completed responses, as 442 had missing data on both counts. The final sample included 1293 men, 2985 women, 39 nonbinary people, and 24 who chose not to answer the gender question. Table  1  summarises the main variables. The categories for education ranged from 0 = 'No schooling completed' to 4 = 'Postgraduate degree', so the point nearest the mean value corresponds to '3 = University undergraduate degree/professional equivalent'. The demographic variables (gender, age, education) were included as covariates in all analyses reported below, though the model coefficients for these covariates are reported only in the full analysis at https://osf.
One of our research questions aimed to examine how general the patterns in the data would be across countries. Our findings indicate that relationships between ideology, trust in science and approval of pandemic measures followed the same pattern in the top 10 countries in our dataset. Still, considerable variation was observed among countries, with the USA appearing to be an outlier in both relations. On the one hand, these findings support previous work showing that conservative ideology is linked to less trust in science in predominantly Western countries  (Gauchat, 2012; Pennycook et al. , 2020). On the other hand, the high variability of responses provides strong reason to examine the links of trust in science with individual behaviour in diverse populations. Another important question for future research is how cross-country differences in political culture and ideology (i.
Funding JS and OD gratefully acknowledge support by the  NOMIS Foundation  through the project " Diversity in Social Environments ". Participant payment for the followup study was funded by  British Academy/Leverhulme  grant  SRG1819\\190996  to JS. GD is funded by  CAP2025 Challenge 4. MEZ is funded by the  Wellcome Trust  grant number  204702. MN is funded by a  UKRI   Future Leader's Fellowship grant  ( MR/T041099/1 ). Open Access funding enabled and organized by Projekt DEAL.
e. , smoking, smokers, etc. ), and 'cigarette. ' In doing so, we followed research that has identified the NYT as arguably the most influential newspaper in the US  (Fiss and Hirsch, 2005). Our initial search resulted in 6,336 articles. In order to validate our sample, we manually skimmed through these articles and discarded those that: had length issues (e. g. , were too short or extremely long), were not related to the US, or were unrelated to the tobacco debates (e. g. , obituaries). After pre-processing the data, our final sample consisted of 3,688 articles with a mean word count of 701. Next, we cleaned the data by filtering out stop-words that carry no thematic meaning (e. g. , 'you,' 'and,' 'I') and may hamper the estimation, as well as words that occur so infrequently that they are unlikely to be representative for a specific topic.
Following standard practice  (Hannigan et al. , 2019) , we stemmed words (reduced 'company' and 'companies' to their stem 'compan') throughout the text corpus. Alternative decisions to make when cleaning the data, which we have not used, are whether to lemmatize the words or whether only to use certain parts of the documents (e. g. , nouns/verbs, see  Hannigan et al. , 2019). For these steps, we chose the opensource statistical software R and its 'stm' package  (Roberts et al. , 2019) , which offers several functions to import/manipulate textual data. 2 Besides preparing the text corpus, the package allows processing additional metadata for each document. Such metadata can be any type of information deemed relevant for exploring relationships between external factors and textual content (e.
g. , authorship, outlet, date, tone, length, etc. ). The critical perspective of CDA provides researchers with the necessary theoretical guidance to identify the appropriate metadata to be included in the STM. For convenience, we refer the reader to the R package 'stminsights'  (Schwemmer, 2018). Most R packages come with a tutorial, which is also available for STM. For creating the text corpus, the reader is referred to https://cran. r-project. org/web/packages/corpus/vignettes/corpus. html. For estimating the STM, the following vignette is very useful: https://cran. rproject. org/web/packages/stm/vignettes/stmVignette. pdf. For additional resources on the 'stm' package, see www. structuraltopicmodel. com For our case, we use two pieces of meta-information, namely the publication year and the main actors present within each article.
This step is essential for identifying key moments of interest, for example, to pinpoint when a specific topic is most or least used. It helps understand how topics evolve and reveals the underlying forces shaping the broader discourses, providing the ground for critical reflection on how the relations between actors evolve in the debates. Step 6: Select a sample to zoom in on At this point, exemplary texts need to be selected to engage in a qualitative analysis of specific features. In CDA, it is typical to select texts based on contextual analysis, but usually without a systematic analysis of the broader dataset. By starting with STM, CDA scholars may significantly enlarge the datasets they can consider, allowing sampling to happen at a later stage in the research process. Since we wish to identify broad patterns based on large textual data and intend to explore actors' legitimation strategies over time in detail, we use CDA to interpret texts that reflect structuring moments within the debate.
, 2020). To combine these approaches scholars need to consider their integration from a methodological and epistemological perspective, similar to our present analysis, an aspect that is often missing when simply transferring quantitative approaches into traditionally qualitative fields. Second, and in line with our call for a mixed-methods approach, we maintain that CDA helps make theoretical sense of STM results. Despite the many advantages of using STM when analyzing large textual datasets, this method must always be combined with higher-level theoretical reasoning and researcher driven interpretation. This issue has become apparent in recent advances, which warn about the dangers and illusions of pure empiricism. Indeed, prior work has argued that different approaches can significantly add to topic modeling's standard applications (e.
validated and theoretically grounded. Whereas STM helps reveal the occurrence of topics and discourses within large datasets, CDA and its analytical techniques enable the researcher to gain in-depth insights. The combination of both approaches yields a fine-grained understanding of broad but theoretically relevant patterns. Accordingly, our model enhances Hannigan et al. 's (2019)  notion of 'rendering' by iteratively zooming in and out of textual data. By doing so, our study makes two important contributions to management research.
By «media», we mean here not only the emergent «mass media», but also other kind of written publications -weekly, literary journals or books -that are in other ways involved in the transnational circulation of information and the  15  Our PhD research seek to analyze the transnational circulation of information and the representations of the French May in Uruguay. In a collective project coordinated by Vania Markarian and Gabriela González Vaillant, we studied the Uruguayan '68 by means of a database of protest events collected in the weekly Marcha: Gapenne, C. (2021). «Protesta estudiantil, medios de comunicación y opinión pública en torno al 68 uruguayo», in González Vaillant, G. and Markarian,  V. (Eds. ). El río y las olas: ciclos de protesta estudiantil en Uruguay (1958, 1968, 1983, 1996).
Acknowledgements. The soil sampling and the analyses of physicochemical properties of soils were supported by the  French Scientific Group of Interest  on soils,  GIS Sol , involving the  French Ministry  in charge of the  Ecological Transition  (MTE), the  French Ministry in charge of the Agriculture and Food (MAA) , the  French Agency for Environment and Energy Management (ADEME) , the  French Biodiversity Agency (OFB) , the  National Institute of Geographic and Forest Information (IGN) , the  French National Research Institute for Sustainable Development , and the  National Institute for Agronomic and Environmental Research (INRAE). We thank all the soil surveyors and technical assistants involved in sampling the sites. Financial support. The PAH analyses were supported by a grant from the  French Observatory for the Pesticide Residues (ORP).
Code availability. The R code used to spatialise the data has been published in a repository of the research institute IN-RAE. This code can be accessed through the following DOI: https://doi. org/10. 15454/BJFUEV  (Saby et al. , 2021). Data availability. The data used in the paper are available at: https://doi. org/10. 15454/WPR2C9  (Froger et al. , 2021). A raster file in Geotiff format containing the geo-refenced data of PAH sums after spatialisation is also available with the data set. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/soil-7-161-2021-supplement.
Research design and methodology Design A descriptive mixed-methods design was used for this quantitative study. Data were collected through a 65-questions online questionnaire. The questions concerned different aspects of teacher practices during the coronavirus pandemic. The majority of the questions (40) dealt with the nature of the distance learning protocol set up by the teachers. They focussed on the cognitive and mediatory components of the teachers' practices asking teachers to describe the adaptations made to their regular teaching practices. The remaining 25 questions focussed on the social, personal and institutional components of the teachers' practices. The information gathered thus comes from teachers' declarations about their practices rather than from direct observation of these practices.
Data collection and instrumentation The questionnaire was administered to high school mathematics teachers during weeks 2-5 of the lockdown in March/April 2020. There were 368 responses. The majority of the Pedagogical continuity participants were experienced teachers (more than 70% had more than 6 years teaching experience). However, of the 20% of teachers working in schools in deprived areas, 40% had less than six years of experience. 82% of the participants declared themselves confident using digital technology. These proportions are representative of the general population of French high school mathematics teachers. The principal statistical treatment involved a multidimensional analysis using multiple correspondences (MCA)  (Lê et al. , 2008; Kostov et al. , 2013). The MCA was carried out on the complete dataset.
Design and Sample The data analysed in this paper were collected via the French "Health Literacy Survey 2019" (HLS19). We conducted a cross-sectional online survey among a sample of the French population aged 18-75 (N = 1003) two weeks after the end of the full lockdown in France (between 27 May and 5 June 2020). Participants were selected from an online nationally representative research panel of households of the French general population developed and maintained by the survey research firm IPSOS (Paris, France). A total of 23,289 individuals were initially invited by mail to fill out the survey. Quota sampling was managed to match French official census statistics for gender, age, size of the population in the area of residence and region. The largest difference between theoretical quotas and our sample was -1.
Data Collection After obtaining informed consent, respondents answered a self-administered online questionnaire broaching five main themes: (1) demographic characteristics (gender, age, level of education, region of residence, financial difficulties, occupation, current health condition); (2) health literacy, ability to navigate the healthcare system and communication with caregivers through new technologies; (3) perception of and familiarity with clinical research; (4) trust in institutions; and (5) knowledge and concerns about the coronavirus epidemic. The items on clinical research focused on familiarity with and attitudes toward clinical trials, but also on perceptions of clinical trials' therapeutic usefulness and of the role they play in health care and knowledge production (Table  1 ).
He/she was considered having a positive attitude of clinical trials if he/she answered 6 or more for their impression of clinical trials. We then used a purposeful selection of the variables to establish a main effect model  [33] , first selecting in the binomial regression variables displaying univariate association with a significance threshold of p = 0. 20 with a chi-square test, then removing from the model non significant variables (threshold of p = 0. 05). We then tested each removed variable for their significance if added. Statistical analyses were conducted using Python (Pandas-Scipy-Statsmodel).
The scientific controversy over the effectiveness of hydroxychloroquine unfolded in the public sphere. Commentaries focused on the findings, methodologies and biases of ongoing trials, but also on the presumed conflicts of interest of their investigators. The government published regular progress reports on the Discovery clinical trial (NCT04315948) set up by the French biomedical research institute (INSERM), and the protocol of this trial was widely criticised by its opponents. These various controversies had an impact on clinical research itself. Most notably, recruitment in the Discovery trial slowed down enough to be covered in the mainstream news  [4]. Despite the accumulation of negative results, many resources were devoted to testing hydroxychloroquine to the detriment of other candidate drugs.
This survey was conducted at different times of the day and on different days of the week and of the weekend. The processing of the data collected was performed using various types of statistical analysis, including, in particular, a Multiple Correspondence Analysis (MCA) type factorial analysis carried out using the software R (R Core Team, 2020) and the package FactomineR  (Lê et al. , 2008). This method of statistical analysis is particularly well suited to the processing of results from questionnaires of this type  (Husson et al. , 2016). It was focused on a set of data (23 variables) relative to declared perceptions and uses of the mountain. These were compared to other variables relative to the intrinsic characteristics of the individuals (11 variables). In order to enable a spatialised survey according to the municipality of residence in the GSCSV, a re-segmentation of the territory in four subcatchment areas was proposed (see Figure  4 ): Aix-en-Provence, Sainte-Victoire north face, Sainte-Victoire south face, Arc valley.
It should be noted that this pattern of distribution results in large part from the choice of site for the application of the questionnaire. The populations surveyed in the other subcatchment areas are distributed as follows: 59 respondents for Sainte-Victoire north face, 95 respondents for Sainte-Victoire south face and 50 respondents for the Arc Valley. Concerning Sainte-Victoire north face subset, the results show that 11 respondents only came from the five northernmost communes. Data from this subcatchment area therefore appear to be dominated by the perceptions and practices of populations in direct proximity to the mountains. This relatively low presence of people from the northernmost communes of the GSCSV on Sainte-Victoire site can also be interpreted as revealing a distance decay effect.
Each department is divided into municipalities and all municipalities with more than 10,000 inhabitants are further divided into statistical zones (IRIS). Fig. 1  shows the spatial extent of the study area with the zoning centers. While the IRIS in the center of Paris can be as detailed as 250 × 250 m the zones are larger the more sparsely populated the territory becomes further away from the Metropolitan core. For the Île-de-France region, we work with eight departments, 1296 municipalities, and 5259 IRIS covering an area of around 12, 000 km 2. Second is the annual updated census data set. It consists of multiple sub-datasets of which three are used in our process: a large microsample of the population amounting to about 30% of all households in France, a detailed data set of commuting relations, which can be aggregated to a flow matrix containing the commuting flows for work and educational purposes between any two municipalities in France, and aggregated zonal information.
The microsample contains sociodemographic information for every covered household and all household members. Spatially, the place of residence of each household is known by IRIS, or municipality if the respective IRIS has less than 200 inhabitants. Furthermore, for all inhabitants of municipalities of less than 10,000 inhabitants, only the department is known. These rules ensure sufficient anonymity of the households included in the microsample, but need to be considered in the population synthesis process. While the aggregated and microsample data is used to synthesize a population of households and persons with sociodemographic attributes and home locations, the commuting data set is used to define work and education places for each person. Household income information for French municipalities can be derived from the public FiLoSoFi data set.
While sociodemographic information is available for every person in the interviewed households, a detailed activity chain is given for one reference person. The activity chains describe in detail which activities the person has performed during one day and how the person has moved between these activities. The HTS is used to define a daily activity chain for every synthetic person. Fifth, data is used which describes the locations at which activities can take place in the region. Those are the address database (BD-TOPO), the enterprise census (SIRENE), the service and facility census (BPE). The address database contains all physical addresses in France in written form and accompanied by their detailed coordinates. In the pipeline it is used to discretize the place of residence of each synthetic household.
The enterprise census is a database of all French enterprises and their facilities including their written address and number of employees. It is used in the pipeline to discretize work places of the synthetic persons. To do so, it is matched with the address database to find the detailed coordinates of every facility  (Hörl and Balac, 2021b). Finally, the service census is an annually updated database which, among others, contains the locations of all educational facilities in France. In the pipeline it is used to assign locations for educational and discretionary activities.
Given knowledge on the anonymization procedure of INSEE, it is possible to artificially find an IRIS for each household for which no distinct identifier is given. In case only the department is known, it means that the household is located in a municipality of less than 10,000 inhabitants in this department. Those are also not covered in sub-level IRIS and, hence, can be treated as IRIS themselves. To define a distinct zone for such households, it suffices to sample one of the relevant municipalities. This sampling can be performed using the total population of the municipalities as the sampling weight, which is known from the aggregated census data. A similar procedure is applied when only the municipality identifier is known. In that case, the household must be located in an IRIS of less than 200 inhabitants, which can also be sampled taking into account the population totals.
Activity chains After income assignment, all data is in place to combine the sampled synthetic population with data from the HTS. The aim of working with the HTS data is two-fold: First, some interesting attributes are not available in the census and the synthetic population at this stage. For instance, it is not known which of the synthetic persons has a driver's license. However, this information is known for every observation of the HTS. At the same time, both data sets feature attributes that have the same meaning and set of discrete values (such as age class, sex, income class). Assuming that those mutual attributes are sufficiently correlated with the unilateral attributes of interest, it is possible to enrich the synthetic population with additional information. This enrichment can be done by finding HTS source observations that are sufficiently similar to the synthetic target persons in terms of mutual attributes.
Their unilateral attributes can then be attached to the target persons  (and, in principle, households). Second, each person from the HTS comes with a full-day activity and trip chain. Assuming again that the sociodemographic attributes are sufficiently correlated with daily activity patterns, we can pose the same argument as above to attach whole chains from HTS data to the synthetic population. In technical terms, we apply a procedure that is inspired by statistical matching algorithms  (D'Orazio et al. , 2006; Namazi-Rad et al. , 2017). First, we define a list of matching attributes  1∶. For each (target ) observation  of the synthetic population, it is then possible to note down a vector of attribute values: 𝐚 𝑡 = ( 𝑎 𝑡,1 , … , 𝑎 𝑡,𝑁 ) (2) Likewise, every HTS (source) observation can be identified by an index  with the respective attribute vectors: 𝐚 𝑠 = ( 𝑎 𝑠,1 , … , 𝑎 𝑠,𝑁 ) (3) Additionally, a weight   is known for each source observation.
Primary location assignment As primary activity types, we consider home, work, and education. The former is performed in a straight-forward fashion: Since an IRIS or municipality is defined for each synthetic person, the data can be linked with the address database. In a first step, all addresses can be matched with the spatial zoning data set containing the shapes of all IRIS and municipalities in the country. Second, for each household, a random address can be sampled from those address locations that are available in the respective IRIS or municipality. Using the commuting matrix, locations for work and education can be assigned. The aim of this procedure is twofold: First, the correct number of people should commute from one municipality to another in the population; second, the commute distance should fit the activity chains that have been assigned to each agent in the previous step.