dataesr/scientific-paragraphs-categorization huggingface dataset:
As model transformation frameworks are mature tools with a large amount of research and development work committed to them, they are ideally placed to take over the task of evaluating access-control policies as a special case of their natural range of use. As mentioned in Section 2. 2, we use ATL as our model transformation framework of choice due to its maturity and tooling. Additionally, ATL uses OCL as its navigation and query language, which facilitates the translation of our UCON policies to ATL. Practically, the translation of UCON policies to ATL transformations follows the translations rules described in Table  2  (note that we reuse the simple Request and Decision metamodels defined in  [21]  to use them as input and output models of our transformation. To the decision metamodel, we add a list of updated values for attributes).
[21]  defines an approach that can handle MAC, DAC, RBAC and ABAC policies and generates a PDP specification. Finally,  [45]  provides a general MDE-based PEP-PDP continuity-enhanced and configurable UCON enforcement engine. With it, they also provide a metamodel, but it is specialized for enforcement and, as a consequence, it is not fully aligned with the original UCON and it does not cover explicitly concepts such as subjects, objects or rights. Table  4  summarizes this related work, showing the access control models covered by the aforementioned approaches. As it can be seen, usage control and concretely, the UCON model is only partially tackled in two works:  [45]  and  [41]  with the limitations discussed above. In short, as far as we know, ours is the first approach that provides complete UCON support in a modeling environment, from the policy definition to its execution, all in a generic way that can be reused no matter the type of software models we are interested in; and a native way that facilitates the interoperation of our solution with all the other existing tools around the EMF framework.
Conclusions and further work This paper presents a model-based approach to facilitate the definition of complex security policies based on the Usage Control (UCON) primitives as part of any software development project. The approach has been implemented on top of the EMF framework and validated by means of a complex running example, showing that our approach is expressive enough to capture complex UCON policies. Moreover some preliminary, promising performance measures have been obtained to show the feasibility of enforcing the modeled rules. Thanks to this bridge enabling the use of UCON in modeling environments, UCON security policies can be added to any modeling toolchain for further analysis, monitoring, enforcement and code generation. As further work, we plan to explore in more detail some of the new application scenarios enabled by our proposal.
For instance, we will link our proposed UCON metamodel with the BPMN one, to facilitate the definition of UCON policies on top of business processes, significantly enhancing the possibility of defining advanced security constraints in business process models. At the UCON DSL level, we will work on a hybrid graphical notation for UCON and on a codegeneration approach to (semi)automatically implement the defined rules on top of existing PEP infrastructures. Empirical studies to validate the trade-offs between UCON and other (simpler but less powerful) languages will also be investigated.
Acknowledgments This work has received funding from the  ECSEL Joint Undertaking (JU)  under grant agreement No  101007260  (TRANSACT), the  Spanish government  ( LOCOSS  project  -PID2020-114615RB-I00  and  AETHER-US  project  -PID2020-112540RB-C44/AEI/10. 13039/501100011033 ) and the  Junta de Andalucía  (METAMORFOSIS and  COPERNICA -P20_01224  projects).
Abstract The relevance that the application of what is learned in Mathematics to everyday situations has in student learning has led us to develop an experiment on teaching the logarithmic function using modelling tasks. Based on this experiment, we intend to characterize the activities of 12th-grade students in the performance of modelling tasks concerning topics of logarithmic functions and identify the difficulties they may present while solving these tasks using a graphing calculator. When adopting a qualitative and interpretative approach, data were collected through students' written records while solving the proposed tasks using the graphing calculator. The results indicate that the students show that the modelling tasks promoted group work and their interest and participation in class.
There is some indication that discrimination as well as low levels of life satisfaction render young people at risk of cyberhate victimization. Adopting a socio-ecological perspective, this paper examines whether supportive family, peer and school environments may buffer against the effects of perceived discrimination and low life satisfaction on cyberhate victimization. Data from four countries (N = 3396) of the EU Kids Online IV survey on children aged 11-17 (51% girls) revealed a positive association between perceived discrimination and cyberhate victimization, but this impact was moderated by supportive family and peer environments. A negative association between life satisfaction and cyberhate victimization was mitigated by peer support.
Participants and Procedure The study utilises survey data from the EU Kids Online IV (EUKO IV) project which focuses on the online risks and opportunities for European children  (Smahel et al. , 2020). Specifically, this study uses survey data from 3396 children and adolescents from four involved countries (the Czech Republic, France, Poland and Romania). All four countries asked questions concerning experiences with cyberhate and collected data from adolescents aged 11 to 17 (M age = 14. 00; SD = 1. 91; 51. 0% girls). Using listwise missing data exclusion, this study utilises the following data: the Czech Republic (n = 1626; M age = 14. 20; SD = 1. 77; girls: 51. 1%), France (n = 657; M age = 13. 63; SD = 1. 82; girls: 45. 5%), Poland (n = 592; M age = 13. 84; SD = 2. 09; girls: 54. 1%) and Romania (n = 521; M age = 13.
99; SD = 2. 12; girls: 53. 9%). Listwise data exclusion did not affect the gender distribution but resulted in a small but significant overrepresentation of older children (0. 53 years, t(4825) = À8. 689, p <. 001, Cohen's d = À. 274). However, both, age and gender were controlled for in our analyses. The presented sample was used in all analyses exclusive of weighting. Descriptive statistics of the representative sample with population weights can be found in the EUKO IV technical report  (Zlamal et al. , 2020). Data collection in these countries took place from October 2017 to May 2018 using CASI/CAWI (computer-assisted self-interviewing and computerassisted web interviewing) which reduces social desirability to sensitive questions (such as cyberhate victimization). The sampling in the Czech Republic, Poland and Romania was carried out via schools, where the data was collected in school classrooms.
Data Analyses Descriptive statistics were performed for all variables to determine means or proportions by country. Multiple regression-based moderation analyses were performed using the PROCESS macro version 3. 4 for SPSS 25  (Hayes, 2018)  applying 5000 bias-corrected bootstrap samples to address the skewed nature of the dependent variable. Cyberhate victimization was entered as the dependent variable and discrimination as well as life satisfaction as predictors. Family, peer and school environment were tested as moderator variables  examining two-way interactions with each of the predictors. Participants' age, gender and dummy coded country variables (using France as reference category) served as control variables. Grand mean-centring was performed for continuous variables. Complete case analyses without the use of weights were performed for all statistics.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This research was co-funded by grants from Facebook, the International Observatory of Violence in Schools for France and the Czech Science Foundation, grant number 19-27828X (project FUTURE). The data for this study were collected within the EU Kids Online IV survey (see  Smahel et al. , 2020; Zlamal et al, 2020).
National parliaments have become important intermediary actors linking the European sphere to domestic concerns and particularities. Although their direct influence on the EU's enlargement process remains limited, they represent a forum for debate and position-taking that integrates domestic public opinion on enlargement and constrains the behaviour of national executives at the European level. By mapping political discourses on enlargement across four different institutional contexts and combining founding members with more recent entrants, we seek to establish broader patterns of discursive framing and to examine the linkages between national discourses on enlargement and the overall direction of European integration. Our empirical analysis builds on a comprehensive, original dataset of over 350 handcoded statements from 120 plenary debates on enlargement towards the Western Balkans 1 held in our four selected countries between 2004 and 2017.
Research design and data We leverage an original dataset of hand-coded statements from national parliamentary debates on EU enlargement to conduct our empirical analysis. Given our objective to distil broad discursive trends, we selected four EU member states that vary regarding their historical ties with the EU, their parliamentary models, and their prevalent views on enlargement. France and Germany are two founding member states and important decision-makers in EU politics. France historically tends to favour a deepening of European integration among a smaller circle of countries, resulting in a growing hesitancy towards admitting further members  (Wunsch 2017). The French Assemblée nationale is viewed as a comparatively weak parliament, in contrast to the institutional strength of the German Bundestag  (Sprungk 2013).
For the selection of debates, we searched the four parliaments' online archives and identified all plenary protocols with any reference to enlargement-related topics. In a second step, we used political claim-making analysis (PCA)  (Koopmans and Statham 1999)  to capture individual statements defining an actor's position about a (potential) candidate's EU accession. 4 We included all claims relating to actual EU membership and candidacy as well as Stabilisation and Association Agreements. Unlike machine learning techniques, our dataset results from a comprehensive coding process based on a detailed qualitative assessment of the plenary protocols, with each parliamentary debate examined and hand-coded by trained country experts. The benchmark for a statement to be included in the dataset was relatively high, since it had to contain both a position and at least one argument justifying the speaker's stance on EU enlargement.
Accordingly, this coding scheme does not capture every single mention of the Western Balkans in the four national parliaments, but focuses instead on actual substantive arguments on EU enlargement. As a result, the statements contained in the dataset allow us to understand how enlargement is discussed, rather than to survey the mere frequency with which it is mentioned. Our dataset captures 353 statements on the political dimension of enlargement from 120 plenary protocols (see Table  A1  for overview). Our empirical mapping of different enlargement discourses relies on qualitative frame analysis. By scrutinizing how actors use political frames to justify their views on further widening, we analyse their particular understanding of enlargement and the construction of European integration in a broader sense.
We aggregate the keywords coded to each statement in our dataset to one or more of the master frames presented in the theoretical section. As described above, the frame typology is set up deductively in relation to the established literature on European integration and EU enlargement to avoid subjectivity in the frame identification. Table  1  provides an overview of the keywords corresponding to the previously identified master frames. Taking the Eastern enlargement round as a starting point, our empirical analysis encompasses the period from 2004 to 2017. This timeframe broadly corresponds to the EU's engagement with the Western Balkans since the formal awarding of a membership perspective for the region at the Thessaloniki Summit in June 2003. Although Croatia successfully concluded its accession negotiations and joined the EU as a member state in 2013, overall progress towards EU accession has been sluggish throughout most of the Western Balkans.
Mapping political enlargement discourses Our study seeks to identify and unpack divergent political discourses on EU enlargement since the 'big bang' accession of 2004. We begin by assessing broad discursive trends across our dataset, before delving more in-depth into the qualitative distinctions within each of the discursive categories we identified. Observing the distribution of different types of discourses across our four country cases (see Figure  1 ), two findings stand out: first, we note a clear dominance of pragmatic discourses in all four countries included in our study. These account for over 60% of statements analysed, while institutional discourses amount to around a quarter to a third of all statements. Discourses based on normative arguments, in turn, remain marginal in all settings.
Second, there is an important discrepancy in the absolute number of statements coded for each of our national parliaments (see Table  A1 ). Whereas the numbers are comparable for France, Hungary, and Poland, our dataset contains more than three times as many statements made in the German Bundestag. Two interpretations may explain this unequal distribution: on the one hand, the German Bundestag formally holds the greatest scrutiny powers of the four included parliaments, which can be expected to translate into a higher level of overall activity when it comes to debating EU-related issues. In Hungary and Poland, national parliaments were still growing into their new role in the post-accession period, which may lead to a lower level of involvement despite reasonably well-developed formal powers (Borońska-Hryniewiecka 2021).
Whereas the Eastern enlargement was couched in a grand narrative of 'European reunification,' the Western Balkan accession lacks such a unifying theme. Regarding temporal variation, we observe a brief downturn of pragmatic statements and a corresponding uptick in institutional concerns in the immediate wake of the Croatian accession (Figure  2 ). Normative concerns remain marginal throughout the period, but are entirely absent in 2004 and drop to near insignificance again at the end of the observed period. Overall, however, we note a striking stability in discursive patterns at the aggregate level, with no notable temporal trends in the relative salience of different types of discourse. Changes over time in individual national parliaments (see appendix Figure  A1-A4 ) are more plausibly explained by the structure of our dataset, with certain years containing few or even no statements for a given category, than by any systematic variation at the national level.
Notes 1. The Western Balkans hold a formal membership perspective and encompass (potential) candidate countries Albania, Bosnia and Herzegovina, the Republic of North Macedonia, Kosovo, Montenegro and Serbia. We exclude Turkey from our discussion given that its quality as a very large and Muslim candidate country has triggered greater reluctance towards its admission in many member states that would risk dominating observed discursive patterns. 2. The total number of EU member states has dropped back to 27 following the United Kingdom's withdrawal on 31 January 2020. 3. In addition to Malta and Cyprus that also joined the EU in 2004. 4. Further information on the coding process including tests on inter-coder reliability is available online: [reference to be added following review]. 5. All direct quotations are authors' translations of the original statements coded in our dataset.
Funding The manuscript was produced as part of the project ' Constructing Europe's Borders: Membership Discourses and European Integration ,' funded by the  Swiss National Science Foundation  under grant number  172558. We are grateful to the  Programme Hubert Curien -Germaine de Staël  for financial support for a French-Swiss research cooperation; Campus France  [ 44535XA ;  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung  [172558].
Cancer incidence We identified any incident cancer in the study period  (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) , by means of several sources described below: the register of the gas and energy company's (Electricité-de-France) medical department, a validated in-house procedure based on the questionnaires and medical records for retired participants  (Goldberg et al. , 2007) , and the French national medico-administrative database of the national health insurance. The company registered all cancers (except non-melanoma skin cancers) occurring during the employment period of all Gazel participants. Each notified cancer case was documented with pathology reports and the date of diagnosis, and coded according to the International Classification of Diseases.
The company did not register cancers occurring after retirement. To address this limitation, from 2008 we obtained dates of diagnosis and types of cancer (ICD-10 and ICD-O-2) as follows: first by recording cancer diagnosis that Gazel participants selfreported in the annual follow-up questionnaires, and then by contacting all of the Gazel participants who gave consent for collecting medical information for validation purposes. We also used data from the national health insurance database that records all data on the use of the French health system leading to reimbursement (from medical appointments to the purchase of prescribed drugs and medical examinations). We linked this database to all Gazel participants who gave consent. This database allowed identifying cancer from data on hospitalizations with dates and diagnoses, and from the "chronic diseases" register (an official health insurance's list of 30 serious chronic diseases, including cancer)  (Tuppin et al.
, 2014) , assessed PM2. 5 concentrations each year between 1989 and 2008 following these steps: (i) the Chimere chemistry-transport model was used to compute annual mean concentrations over Europe (on a 30x30 km grid) and France (on a 10x10 km grid). This model was fed by the European Monitoring and Evaluation Program emissions inventories, point sources, road emissions, and biogenic emissions. (ii) Mesh refinement and data assimilation with geostatistical analyses were then applied to estimate concentrations on a 2x2 km grid. For each participant and each available year, we estimated PM2. 5 exposure as the value at the residential address for both exposure assessment methods.
Lifestyle variables: time-varying variables for tobacco (cumulative smoking pack-years and smoking status), alcohol intake (abstinent, non-daily drinker, light drinker, moderate drinker, heavy drinker, unclear pattern), family status (single or not), body mass index (BMI in kg/m 2 ). Two variables were included only at specific time points: passive smoking at home or at work (yes or no) in 1990 and 1996, and fruit-vegetable intake (never or less than once a week; once or twice a week; more than twice a week but not every day; every day or almost) in  1998, 2004, 2009 and 2014. We computed these variables as time-varying, attributing the data collected in 1990 and 1996 to each year of the periods  1989-1995 and 1996-2015 for passive smoking, and the data collected in 1998, 2004, 2009 and 2014  to each year of the periods  1989-1998, 1999-2004, 2005-2010, 2011-2015  for fruit and vegetable consumption.
Imputations Our dataset included some missing values (from 21% for passive smoking to 29% for BMI). We imputed missing values longitudinally, considering we had several years of follow-up by participant. To do so, we chose a 2-level imputation model with participants as clusters using the packages MICE (van Buuren and Groothuis-Oudshoorn, 2011) and MICEADDS  (Robitzsch et al. , 2018). We imputed all variables except exposures to PM2. 5, and contextual variables (but we still used them to impute all the other variables) using the functions "2l. pmm" and "2l. only. pmm" for time-varying and time-independent variables, respectively. We obtained a satisfactory convergence after 10 iterations for the 10 datasets (Figure  S3 ).
Statistics Comparing estimates of exposure to PM2. 5: Estimates from the two exposure assessment methods, applied to each participant for each year, were compared as follows. First, we calculated Spearman's correlation coefficients a) for the complete dataset and b) on subsets according to the year and region to check for any differences. We plotted Bland-Altman graphs to check for any bias between the two methods, again using the complete dataset and subsets. Since the two exposure assessment methods considered the influence of traffic and roads differently, we also explored correlations in population subsets defined by distance to the nearest major road. We defined subsets as (i) the European NUTS-1 regions, and (ii) we divided the distance to the major road into quartiles, and further subdivided the first quartile at 150m to explore near-road exposures.
We assumed a linear relationship between continuous variables and each outcome. Sex was included as a strata variable as it did not meet the proportional hazards assumption. For all-site cancer incidence, we conducted sensitivity analyses by (i) further adjusting for the area-level French deprivation index in addition to individual SES  (Temam et al. , 2017), (ii)  restricting the study population to the participants with only the most precise geocodes throughout their follow-up (i. e. addresses-level geocodes, see "Study population"), and implementing several alternative ways to handle missing data by (iii) considering missing data as a category, (iv) imputing missing data as median/mode, or (v) conducting complete-case analyses. To detect any effect modification by sex, smoking status, distance to the nearest major road, and population density, we restricted our analyses to the following groups: female or male, ever or never smokers, nearest major road within or beyond the median distance of 500m over the follow-up, and following the urban classification at municipality-level (see covariables): solely urban (separately in the Paris region vs out of the Paris region) or solely semi-urban or solely rural.
For lung cancer incidence, due to the small number of cases, we did not perform any sensitivity or stratified analyses. We conducted all analyses with the R software version 3. 5. 1 (R Core Team, 2018) and the 'survival' package  (Therneau, 2015; Therneau and Grambsch, 2000). Unless specified otherwise, we used multiple imputations by chained equations to conduct all the analyses and pooled estimates using Rubin's rules.