dataesr/scientific-paragraphs-categorization huggingface dataset:
A number of authors link this diffusion of managerial modes of performance measurement on the micro scale with a global turn in the relationship between knowledge and power in law and justice, which enables the "transformation of social facts of legal relevance (e. g. ordered empirical data about the rule of law) into standards of behaviour (i. e. rule of law benchmarks)" (Restrepo Amariles 2015, 12). In the next paragraph, we highlight this link existing between the managerial turn in public policy and the production of legal indicators at the International and European level focusing on two projects of quantification of judicial systems that assess the efficiency and quality of their functioning: the 'Evaluation Report of European judicial systems' developed by the CE-PEJ, and the 'EU Justice Scoreboard' of the European Commission.
e. on the creation of an area of equivalence for statistical comparison  (Desrosières, 2011). A Dutch court management expert and member of the group produced a study of the existing frameworks of evaluation of judicial activity. The Dutch and English frameworks of court evaluation, created with the NPM reforms during the late 1990s, and the framework used by the World Bank to compile its justice sector database, were used for proposing a set of indicators. However, part of the group considered this first project "too bureaucratic and detailed for member countries to be filled 6 ". Though, rather than the intergovernmental bargaining pointed out as a topical tension of international committees, it was the deliberative equilibrium between conceptions of judicial systems that was at stake in the group  (Joerges & Neyer, 1997).
Admitting that infringement procedures are too technical and too slow to react to high-risk situations concerning the rule of law, and considering the Article 7 TEU procedure as a nuclear option 10 , the Commissioner Reding proposed to 'add to the economic and social benchmark in the European Semester a new mechanism for measuring, comparing and benchmarking the strength, efficiency and reliability of the justice systems in all Member States 11 '. The Commissioner proposal referred to the creation of a document in the form of an annual scoreboard resuming data on judicial systems in the EU to pinpoint malfunctions in their organisation and design concrete reform venues with member countries under the European Semester recommendations programme. In late 2012, DG Justice, as chef de file of the project, was charged with the coordination of the drafting of the 'EU Justice Scoreboard' within the Commission.
A newly set unit created in January 2013 and composed of a dozen legal experts hired from other departments of the Commission was charged with the coordination of all Commission activities related to judicial organisation reforms, i. e. monitoring of judicial reforms in the European Semester, and the new-born EU Justice Scoreboard. Most of them are lawyers with no experience in the specific field of socio-legal measuring and evaluation 12. The modus operandi for the Scoreboard involves substantial contributions from other departments of the Commission, notably from DG ECFIN for statistical assistance 13. Moreover, data used to compile the Scoreboard comes mainly from external sources such as CEPEJ, the European Network of Councils for the Judiciary, the World Bank, the World Economic Forum and the World Justice Project.
To the despair of CEPEJ experts, in 2010 the UK government cut its legal aid funds and justified its choice with CEPEJ data, proving that the UK had the biggest budget in Europe allocated to it. It is arguable that the use of CEPEJ data done by the EU Commission falls in the same category in the opinion of many CEPEJ members who criticise the economic-oriented approach of the Scoreboard. The report worked as well as the catalyser of attention over CEPEJ and its other activities. Within CEPEJ, the SATURN Centre for judicial time management (another expert group) develops benchmarking tools for the management of court timeframes. These practical tools found interested audiences in courts at the national and local level, which lead CEPEJ to participate in various cooperation programmes within specific courts or with national authorities.
71). In 1954, the magazine Mundo Peronista estimated that the women's section had a national membership of 60,000 and the men's 42,000, representing just over 50% of all secondary school students  (Manzano, 2014, p. 24). These figures are significant, but they show that the UES was far from covering the entire secondary school population. In addition, official data were very likely to have been exaggerated by the government: membership did not always go hand in hand with active participation in the organization  (Cammarota, 2014, p. 154-7). Argentinean secondary school students did not have any obligation to join or participate in the activities of the UES. However, the authors of the 1958 investigation report stated that membership was neither free nor spontaneous and that an enrolment campaign was orchestrated from the Ministry of Education.
Background: Adverse childhood experiences (ACEs) have been associated with poorer health across the life course. Previous studies have used cumulative risk scores (ACE scores) or individual ACEs but these two approaches have important shortcomings. ACE scores assume that each adversity is equally important for the outcome of interest and the single adversity approach assumes that ACEs do not co-occur. Latent class analysis (LCA) is an alternative approach to operationalising ACEs data, identifying groups of people co-reporting similar ACEs. Here we apply these three approaches for ACEs operationalisation with inflammation in childhood with the aim of identifying particular ACEs or ACE combinations that are particularly associated with higher inflammation in early life. Methods: Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) we compare ACE scores, single adversities and LCA-derived ACE clusters in their relationships with Interleukin-6 at age 9 (n = 4935) and C-Reactive Protein (CRP) at age 9 (n = 4887).
Dataset In this study we used data from the Avon Longitudinal of Parents and Children (ALSPAC). ALSPAC is a prospective pre-natal cohort from the Avon region of South-West England. Pregnant women with expected delivery dates between 1st April 1991 to 31st December 1992 (n = 14,541, 71. 8 % of eligible pregnancies) were included in the study  (Boyd et al. , 2012; Fraser et al. , 2013). At approximately 7 years of age the sample was boosted with children with eligible birth dates who were not part of the initial sample. The total sample size for analyses using any data collected after the age of seven is therefore 15,454 pregnancies, resulting in 15,589 foetuses. Of these, 14,901 were alive at 1 year of age. The present study uses information from surveys from birth through to 17 years of age.
Covariates Gender was included as a potential effect modifier in our analyses. Other covariates included a priori confounders which were also associated with both our ACE measures and inflammatory markers. These were whether the mother had smoked during pregnancy, the mother's marital status at the time of birth of the cohort child (single, married or divorced/separated/widowed), the mother's highest educational qualification by time of birth of the cohort child (categorised as Certificate of Secondary Education (CSE) or Ordinary (O)-Level, vocational qualification, Advanced (A)-Level, or degree or higher qualification), household crowding index (number of persons per room, categorised as 'crowded' if >1 persons per room), and the mother's EPDS score during pregnancy. Please note that the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool http://www.
Multiple imputation As missing information is a key source of bias, particularly in longitudinal studies  (Sterne et al. , 2009) , we accounted for missing data using multiple imputation by chained equations. The imputation models included all analysis variables plus variables likely to be predictive of missingness, such as socioeconomic factors, ethnicity, and indicators of health. We imputed missing information for all cohort members with either IL-6 or CRP observed at age 9 (n = 4935) and restricted the subsequent analyses to those who had each outcome observed, following the Multiple Imputation then Deletion approach  (Von Hippel, 2007). Twenty imputed datasets were created, and the distribution of observed and imputed data was similar (Table  1 ). For transparency, we also provide the findings from the complete case analysis in Appendix B.
Lower values of the AIC, BIC and SSABIC indicate a better fitting model. Also an entropy value approaching 1 suggests better distinction of classes. In deciding on the best fitting model we gave preference to improvements in the BIC, as recommended (Nylund-Gibson and Young Choi, 2018). Upon selecting the optimal class solution, cohort members were assigned to their most likely class, thereby creating a categorical variable for use in subsequent analyses. A latent profile plot was constructed for the final class solution to aid with the qualitative interpretation of the derived classes. Descriptive characteristics are reported for those who have CRP or IL-6 observed at age 9 (n = 4935). a Only pooled percentages are presented for the imputed data as the specific Ns will vary across imputed datasets.
, 2017). Future research should therefore investigate whether ACEs are related to these markers of inflammation across the life course, but particularly in early life. Whilst being a useful method for identifying ACE clusters, LCA is data-rather than theoretically-driven. The findings might also be dataset-specific, although with the increasing application of this method to multiple datasets some similarities in LCA-derived clusters are emerging. This study has many strengths. First, we used a large longitudinal population sample from the UK to explore the relationships between ACEs and inflammation in. early lifeone of the largest studies of its kind. Second, given that much of the research into ACEs and biological sequelae has been conducted on adult populations, there has been a reliance on retrospective ACE reports  (Danese, 2018).
Funding This work was supported by the  Wellcome Trust   ALSPAC core programme grant  [ 102215 ]. RL is supported by the  UK Economic and Social Research Council (ESRC)  (grant number  ES/P010229/1 ). EI is funded by the  ESRC-BBSRC   Soc-B Centre for Doctoral Training  ( ES/P000347/1 ). LDH is supported by a  Career Development Award  from the  UK Medical research Council  ( MR/M020894/1 ). The  UK Medical Research Council and Wellcome  (grant ref:  217065/Z/19/Z ) and the  University of Bristol  provide core support for ALSPAC. This publication is the work of the authors and RL will serve as guarantor for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (http://www. bristol. ac. uk/alspac/external/documents/grant-acknowle dgements. pdf);this research was specifically funded by the  National Institute of Health  ( R01 DK077659 ).
We show that: (1) preserved pollen assemblages are clearly imprinted on the extremities of the ecosystem range but mitigated and unclear on the ecotones; (2) for both proxies, inferred relationships depend on the geographical range covered by the calibration database as well as on the sample nature; (3) local calibrations, even those derived to the low range of climate parameters encompassed in the study area, better reconstruct climatic parameters than the global ones by reducing the limits for saturation impact, and (4) a bias in climatic reconstructions is induced by the over-parameterization of the models by addition of artificial correlation. We encourage the application of this surface calibration method to consolidate our understanding of the Holocene climate and environment variations.
The aim of this study is to take advantage of new, modern surface sample datasets in Siberia and Mongolia to propose an adapted calibration of pollen and bacterial biomarker proxies for cold and dry environments. For that purpose, local calibrations are compared with large calibrations to infer the influence of calibration scale and proxy types on derived climatic parameters. Our approach is summarized in the following steps: 1. Collection of a new set of modern surface samples for Mongolia with homogeneous characterisation of their bioclimate environment. S1 ). Depending on aridity and vegetation at each site, a soil or a moss polster was sampled. Moss acts as a pollen trap recording a three to five-year mean pollen signal  (Räsänen et al. , 2004). In drier areas, the soil surface samples have the same function, in spite of a lower pollen conservation  (Lebreton et al.
This diagram was plotted during the pollen count using PolSais 2. 0, software developed in Python 2. 7 for this study. The rarefaction curve was fitted with a logarithmic regression analysis. The counter was suspended whenever the regression curve reached a flatter step  (Birks et al. , 1992). A threshold for the local derivation at dx/dy = 0. 05 was set. The total count is usually around n ∈ [350; 500] grains for steppe or forest and n ∈ [250; 300] for desert slides. Among all of the pollen-inferred climate methods, the MAT and the WAPLS were applied in this study on 4 different modern pollen datasets. The MAT consists of the selection of a limited number of analogue surface pollen assemblages with their associated climatic values. (Jackson and Williams, 2004) ; while the WAPLS uses a Weight Average correlation method on a limited number of Principal Components connecting the surface pollen fraction to the climate parameters associated  (Ter Braak and Juggins, 1993; Ter Braak et al.
, 1993). The first dataset, called New Mongolian-Siberian Database (NMSDB), is composed of pollen surface samples analysed in this study (N = 49, Fig. 2 ). The second one is the same subset aggregated to the larger Eurasian Pollen Dataset (EAPDB) compiled by  Peyron et al. (2013 Peyron et al. ( , 2017)). From this dataset of 3191 pollen sample sites, a pollen-Plant Functional Type method was applied to determine the biome for each sample according to the actual pollen rain  (Prentice et al. , 1996; Peyron et al. , 1998). Then, only the Cold Steppe (COST) dominant samples were extracted from the main dataset and aggregated with the NMSDB to produce the COSTDB (N = 482 sites). Finally, a scale-intermediate dataset of samples located within the Mongolian border merged with the Mongolian New dataset is presented as MDB (N = 151 sites).
SIG Bioclimatic Data Because Mongolia and Siberia have relatively few weather stations (Fig. 1. A), climate parameters were extracted with R from the extrapolated climatic database WorldClim2  (Fick and Hijmans, 2017). We used Mean Annual Precipitation (MAP, Fig. 1. B), Mean Annual Air Temperature (MAAT, Fig. 1. C), as well as temperatures and precipitations for spring, summer and winter (T spr , P spr , T sum , P sum , T win and P win ), Mean Temperature of the Coldest Month (MTCO) and the Mean Temperature of the Warmest Month (MTWA) in this study to characterize the actual climate. The elevation data and the topographic map originate from the ASTER imagery (Fig. 1. A). The biome type for each site derives from the LandCover database  (Olson et al. , 2001) , classification and field trip observations.
Statistical Analyses GDGTs and pollen matrix were analysed with a Principal Components Analysis (PCA) to determine the axes explaining the variance within the samples. The biotic values (pollen and GDGTs) were also compared to abiotic parameters (climate, elevation, location and soil features) by the way of a Redundancy Analysis (RDA). The regression models were run with the pvalue < 0. 05 for the relevance of the model, the R 2 for the level of correlation between the variables, the RMSE to determine the climate error of the models and Akaike's information criterion (AIC) to quantify the over-parameterization effect of multiple regression models  (Arnold, 2010; Symonds and Moussalli, 2011). All the statistical analyses were performed with the Rcran project, using the ade4 package  (Dray and Dufour, 2007)  for multivariate analysis.
All the plots were made with the ggplot2 package  (Wickham, 2016)  or the Rioja package  (Juggins and Juggins, 2019)  for the stratigraphic plot and the pollen clustering using the CONISS analysis method  (Grimm, 1987). M A A T ( °C) d[, i] yvar E le v a ti o n ( m. a. s. l ) • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • MRUT1M02 MRUT1M04 MRUT1M07 MRUT1M06 MRUT1M01 MRUT1M09 MRUT1M08 MRUT1M10 MRUT1M03 MRUT1M12 MRUT1M11 MRUT1M05 MMNT2M04 MMNT2M06 MMNT1M01 MMNT2M05 MMNT2M03 MMNT2M05' MMNT2M07 MMNT2M02 MMNT1M02 MMNT5M05 MMNT5C01 MMNT3M04 MMNT5M06 MMNT5M07 MMNT3M03 MMNT3S05 MMNT5C12 MMNT5C11 MMNT5C03 MMNT2M01 MMNT5S04 MMNT3S02 MMNT3S01 MMNT4S01 MMNT4S14 MMNT4M09 MMNT4M10 MMNT4S04 MMNT4S12 MMNT4S11 MMNT4S03 MMNT4S02 MMNT4S07 MMNT4S08 MMNT4S05.
MAT and WAPLS Results To reconstruct bioclimatic parameters from pollen data, MAT and WAPLS methods were applied on the four scales, modern pollen datasets and the ten climate parameters (Table  1 ). All these methods can be run with n ∈ [1; 10] parameters: the number of analogues for MAT and the number of components for WAPLS. The best transfer functions among all of them were selected by the following approach: in a first step, for each climate parameter the methods fitting with the higher R 2 and the lower RMSE were selected. Then, in case the highest R 2 and the lowest RMSE were not applied for the same number of analogues or components, we chose the method presenting the lower number of parameters. Despite the small number of parameters relative to the number of observations, the method fits well (Arnold, 2010, table  1 ).
GDGT Variance in the dataset Iso-GDGTs are dominated by GDGT 0 and crenarcheol (X percent in relative abundances, respectively, in Fig. 4. A). Since the majority of these molecules are thought to be produced in the lake water column  (Schouten et al. , 2012)  (2) [br --GDGT tot ] moss = 82 ± 76ng. g -1.
, 2009a, b; Bunting and Hjelle, 2010; Prentice, 1985). For MAT and WAPLS regression models, the same issue holds true. The responses of the eight over-represented taxa to climate parameters are different in the three geographic ranges. The linear tendency allows for checking the main trends between taxa distribution and climate parameters, despite the weak linear regressions (pvalue > 0. 005 and R 2 < 0. 4, in Fig. 8 ) For the majority of these taxa, the trend is the same, independent of the database size (Larix spp. and Cyperaceae percentages increasing with weaker MAAT, or Amaranthaceae and Pinus sylvestris percentages increasing with higher MAAT). However, due to the shift between pollen types and their associated vegetation (i. e. poaceae-pollen signal similar for a wide diversity of Poaceae communities with very contrasted ecophysiological features), trends are controlled in some peculiar cases by the geographical clipping of the DB.
Sample sites MAAT models Cross-value Mongolian moss polsters Gobi desert soil Siberian moss polsters M M N T 4 S 1 4 M M N T 5 C 0 1 M M N T 5 C 0 3 M M N T 5 M 0 5 M M N T 5 S 0 4 M R U T 1 M 0 1 M R U T 1 M 0 2 M R U T 1 M 0 3 M R U T 1 M 0 4 M R U T 1 M 0 5 M R U T 1 M 0 6 M R U T 1 M 0 7 M R U T 1 M 0 8 M R U T 1 M 0 9 M R U T 1 M 1 0 M R U T 1 M 1 1 M R U T 1 M 1 2 Sample sites Δ MAAT (°C) MAATmr1 MAATmr2 MAATmr3 MAATmr4 MAATmr5 MAATmr6 MAATmr7 MAATmr8 MAATmr9 M A A T m r1 M A A T m r2 M A A T m r3 M A A T m r4 M A A T m r5 M A A T m r6 M A A T m r7 M A A T m r8 M A A T m r9 MAAT models    Concerning modeling transfer functions, WAPLS performs better for the local database than for the MDB and COST database (Table  1 ). On these subsets, the WAPLS RMSE and R-square values are even higher than for the MAT transfer function.
Issues in Modelling Mongolian Extreme Bioclimate Firstly, the commonly used br-GDGT indexes (MBT and CBT) are not relevant for arid areas with MAP < 500mm. yr -  because of the relation between low soil water content and soil br-GDGT preservation and conservation interferes in the br-GDGT / climate parameters  (Dang et al. , 2016). Moreover, the main issue in climate modelling in Mongolia is the narrow relationship between MAAT and MAP. Because of the climatic gradient from dry deserts in the southern latitudes to wet taiga forests in the northern ones, MAAT and MAP maps are strongly anti-correlated (Fig. 1 B  and C ). This correlation could also be a bias resulting from the interpolation method of the WorldClim2 database. In fact, there are very few weather stations (Fig. 1. A,  Fick and Hijmans, 2017)  on the large Mongolian plateau area and a great diversity of mountain ranges interrupting them.
To reduce the signal/noise ratio, a wider diversity of sample sites should be added as initial inputs in the models. This raises the question of the availability of reliable samples in desert areas. The soil samples in the steppe to desert biomes are often https://doi. org/10. 5194/bg-2019-475 Preprint. Discussion started: 30 January 2020 c Author(s) 2020. CC BY 4. 0 License. very dry and these over-oxic soil conditions are the worst for both pollen preservation  (Li et al. , 2005; Xu et al. , 2009)  and GDGT production  (Dang et al. , 2016). br-GDGT concentrations in moss polsters and sediments are thus higher than in soils in our database. The soil of the Gobi desert also has a high salinity level which is also a parameter of control on br-GDGT fractional abundance  (Zang et al. , 2018).
Conclusions The palaeoenvironmental and palaeoclimatic signals present several uncertainties which can misguide the interpretation of past variations. This study shows how both a multi-proxy approach and accurate calibration are important in combating these biases. We propose a new calibration for Mean Annual Precipitation (MAP) and Mean Annual Air Temperature (MAAT) from br-GDGTs as well as a new pollen surface database available for transfer functions. The correlations between pollen rain and climate on one hand and br-GDGT soil production and climate on the other are visible but are still mitigated by the complex climate system of arid central Asia and the diversity of soils and ecosystems. Precisely, each of our proxies seems to be more narrowly linked to precipitation (MAP) than temperature (MAAT) counter to the majority of calibrations in the literature.
The nature of the samples considered (soil, moss polster and sediment top-core) also greatly affected these correlations. The calibration work in the extreme bio-climates of the Siberian basin and Mongolian plateau is difficult because of the low range of climate values, despite the climate diversity ranging from cold and slightly wet (north) to the arid and warm (south). The MAAT and MAP values do not remarkably spread in the vectorial space, which makes harder to distinguish the linear correlation against variance noise. Moreover, this range of values is close to the lower saturation limit of the proxies, which makes the accurate local calibration tricky but necessary. The local calibrations also suffer from the reduced size and small geographic extent of the dataset. The vegetation cover, extending from a high cover taiga forest to nude soil desert cover, also buffers the climate signal and the GDGT / pollen response.