dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010). This is sometimes referred to as n ¼ all. This has obvious advantages for the study of small subgroups and certain rare events. 3 Administrative data may also provide a means to access information on those groups who may be the least likely to take part in primary social science research (e. g. individuals from disadvantaged social groups). The large sample sizes of administrative social science datasets may enable the use of novel analytical approaches (e. g. quasi-causal methods) which take advantage of variations in the experiences of groups within these large samples (see  Dunning, 2012). Administrative social science data may also be particularly useful for studying issues that individuals might be reticent to disclose to a primary researcher (e. g. mental health problems or substance abuse)  (Goerge and Lee, 2001).
g. years of experience) were recorded. Chetty, et al. (2011a,b)  linked this experimental data to administrative records to examine the effects which these educational experiences had on outcomes in adulthood. They found that kindergarten test scores were highly correlated with outcomes such as earnings at age 27, college attendance, home ownership and retirement savings. They were also able to identify that pupils in smaller kindergarten classes were more likely to attend college, and that pupils who had a more experienced teacher in kindergarten had higher earnings at age 27. In more recent work,  Chetty et al. (2014)  examined intergenerational earnings mobility in the U. S. using administrative tax records. They constructed a series of cohorts with earnings data on tax records for both parents and their children in adulthood.
Data analysis When administrative social science data are organised for conventional social science research (e. g. the application of multivariate techniques such as statistical models) they are indistinguishable from the familiar rectangular variable by case matrices from conventional social surveys, where a variable is recorded in each column and each case is allocated to a row. 4 ltimately the challenges we will face in the analysis of administrative social science data are indistinguishable from those faced in the analysis of large-scale social surveys. For example, if an administrative dataset has repeated measurements on the same individuals the data will not be independent and identically distributed  (Baltagi, 2008). To tackle data analysis challenges administrative data analysts should not ignore the helpful lessons that have emerged from decades of statistics and econometrics.
In most instances social science administrative datasets are observational, and they are not collected as part of an overall experimental design or experimental data collection protocol. The challenges associated with drawing causal inferences from observational social science data are well known (see  Manski, 1993; Winship and Morgan, 1999). Multivariate methods, for example from the generalized linear mixed model family, provide improved statistical control for analyses of observational social science data (see  Hedeker, 2005). We conjecture that statistical models (e. g. regression models) of observational data are best considered as 'sophisticated descriptions'. 5 There are a set of techniques emerging from econometrics which are aimed at providing partial solutions to the challenges of drawing inference from observational social science data (for an accessible review see  Angrist and Pischke, 2008).
We can conceive of some circumstances where the nature of the social science administrative data facilitate quasiexperimental analyses. For example geographic variations in policies or temporal differences between policy interventions, might possibly provide a natural experiment. In other situations matching techniques or discontinuity designs might also be possible using administrative social science data. The frequent lack of a suitable set of background or auxiliary measures within administrative datasets is likely to constrain the use of many of the more advanced statistical approaches that have currently been proposed to assist researchers in drawing causal inference from observational data. The impact of the size of administrative datasets is a particular issue that researchers should be cognisant of.
Nevertheless administrative data are characterised as having a large n, but a relatively small number of variables (k). Therefore the dataset's dimension is less of a concern in administrative data analysis, compared with the analysis of other forms of 'big' data that have both large n and k. Specialised statistical data analysis packages such as Stata and R are more than capable of handling datasets with a large number of observations. Stata MP, run on a modern multi-processor computer, can analyse 10 to 20 billion observations 6 with thousands of variables. This is more than adequate to analyse a dataset containing an observation for each of the world's population. On a practical level, the analysis of very large datasets will undoubtedly require powerful computing capacity, and the time taken for some computations to be completed may be extended.
If facing computing problems in the analysis of large datasets, a naive solution is to conduct the analysis on smaller samples of the data. Researchers must consider the effects on results of working with a reduced dataset. Researchers should also be suitably cautions to ensure that sub-samples appropriately represent underlying structures in the main dataset. When working with subsets of data we would recommend that researchers undertake sensitivity analyses and make these results transparent. It may also prove useful for researchers to consider insights from the methodological literature on sampling and re-sampling when undertaking analyses with subsets of large datasets (e. g. Kish, 1965). Another alternative is that researchers may resort to using high performance computing (e. g. supercomputers) that are equipped with very high speed processors and large memory capacities.
Importantly, from a statistical perspective the analysis of large datasets increases the need for researchers to be aware of the limitations of simplistic significance tests, and their associated p values. For example when a standard linear regression model is estimated using an extremely large dataset the standard error calculated for a beta can be very small. Using a conventional statistical test of significance this can result in the potentially misleading conclusion that the effect of the variable (net of the other variables in the model) is very important 7 (see  Lin et al. , 2013; Sullivan and Feinn, 2012). This is not so much a problem of large samples, but a problem with how significance tests have come to be regarded in social science research. Discontent over the use of significance tests, is growing and is not specific to the analysis of large samples (see  Carver, 1978; Gorard, 2015a,b; Johnson, 1999; Kühberger et al.
Data management We have noted that, in contrast to traditional types of social science data, administrative social science data may be less systematic and require more data enabling by researchers to facilitate data analysis (see  Einav and Levin, 2013; Goerge and Lee, 2001). Data enabling comprises tasks associated with preparing and shaping data for analysis. These tasks include restructuring datasets, and recoding and constructing variables (see  Long, 2009; Mitchell, 2010). When using administrative social science data, the process of data management may be more complicated and time demanding than when using a social survey that has been primarily collected for social science analyses and has been enabled by data collectors or by a social science data archive or provider. The time demands associated with enabling administrative data for social research will be especially burdensome when researchers are using large volumes of data from multiple administrative systems.
Data generation process Administrative social science data were not collected for research purposes and therefore are not documented in order to support data analysis. In order to fully understand administrative data records, researchers must put effort into uncovering the data generation processes which have governed how these data have been created. Gomm (2008)  highlights that ethnostatistics may play an increasingly important role in the effective use of administrative social science data. Ethno-statistics are studies which have traditionally been conducted to understand the way facts about people are socially produced  (Black, 1994; De Zwart, 2012; Gephart, 2006). Ethno-statistics studies have the potential to be extremely valuable in investigating and documenting the process through which administrative social science data are produced.
This is a clear example of where the investigation of data provenance proves to be critical. Without investigation into the data collection process the researchers would not have appreciated this data recording decision, and would ultimately have reported erroneous research findings. Gomm (  2008 ) also describes the importance of understanding the units included in an administrative dataset. He gives the example of counting the number of psychiatric in-patients over a period of a year. This may at first seem like a straightforward measure of the extent of severe mental illness. In-patient psychiatric care comes at the end of a process involving an individual's decision to consult a doctor, a doctor's diagnostic behaviour, and the diagnostic behaviour of the psychiatrist which an individual may or may not have been referred to.
Therefore there are multiple interactions within the process of an individual reaching in-patient psychiatric care which mean that the simple count of psychiatric in-patients may not be either a reliable or a valid measure of severe mental illness. Administrative social science data will also reflect a system that is itself responding to an ever changing policy context. Changes in administrative systems (e. g. changes in the financial assistance available to unemployed individuals, or the examinations undertaken in schools) may lead to changes in measures. Therefore it is highly desirable that researchers develop a clear biographical understanding of the administrative system in order to appreciate how information is collected and how measures are developed, at the same time as understanding how cases are included in datasets and how data may change over time.
Goerge and Lee (2001)  emphasise that the degree of error varies between administrative data systems, and between items within an administrative data system. Therefore they encourage researchers to assess each new administrative social science dataset individually for each new research question at hand. Goerge and Lee (2001)  encourage researchers to question the original motivation for collecting the administrative data. This can prove to be consequential, for example data collected for financial purposes often produces the most reliable information as there were clear incentives for the data collectors to ensure that the data recorded was accurate. However, the inaccuracies that individuals detect and the errors that emerge in transactions with welfare benefits agencies, tax authorities, transport agencies, health services and local authorities, coupled with the errors, miscalculations and inaccuracies that occur in transactions with service providers such as banks, credit card companies, utility companies, and delivery and transport providers, should all hang a reasonable question mark over the quality of some administrative data for social research.
Data linkage In some northern European countries, such as Norway, Finland and Sweden an effective infrastructure is already in place for the linkage and sharing of administrative data for research purposes (see United  Nations, 2007). The availability of consistent unique identification numbers in these countries is a major advantage for the facilitation of administrative data research  (Einav and Levin, 2013). In countries like the UK and the US the lack of consistent identification numbers across administrative systems means that linking together the pieces of information required to answer a social research question is usually much more complex. With consistent unique identifiers deterministic data linkage is possible, this is because there is a perfect match between individuals on two datasets.
Without unique identifiers researchers may have to rely on other methods such as probabilistic linkage based on a series of weaker identifiers (e. g. name, date of birth, gender). With probabilistic data linkage there is a risk that linkage is not accurate, introducing error into the analysis  (Goerge and Lee, 2001). There have been statistical developments made in dealing with possible linkage errors when using linked data (see  Belin and Rubin, 1995; Lahiri and Larsen, 2005; Scheuren and Winkler, 1993). The accuracy of probabilistic data linkage will vary from dataset to dataset, and some administrative datasets will completely lack the identifiers required to enable linkage. Administrative data researchers must carefully consider the quality of the linkage between their datasets, and the influence which this may have on their substantive results.
Administrative social science data offer the opportunity to study policy changes, social problems and societal issues using information which may not routinely be available in social surveys. The large size of many administrative social science data resources may offer the opportunity to study sub-groups, and could potentially lead to analytical approaches such as quasiexperimental methods being used more routinely. The re-purposing of these data could also result in long term savings for government departments, and social science data producers. As administrative social science data are not collected for the purposes of research, these data are generally more messy and complex than traditional social science datasets. Researchers should therefore not underestimate the amount of time and effort they will need to spend undertaking data enabling tasks to prepare administrative data for analysis.
The importance of developing good data enabling skills is often overlooked, especially when teaching data analysis to students. We suggest that these skills are important if social scientists are to effectively utilise administrative social science data. The role of social scientists as data detectives is particularly important when analysing administrative data. Researchers will need to exert effort to build an understanding of the 'biography' of the administrative data they are using. Understanding the processes of how and why administrative data are collected will be central to assessing the data's quality and its suitability for social research. Big data has the potential to change the landscape of social science research, and administrative social science data offers particular benefits, however we conclude that for administrative social science data to have a suitably full impact there must be a step-change in research practices and research must routinely ensure that work undertaken using administrative social science datasets is efficient, transparent and reproducible.
2014). Our method first extracts a clean CMB temperature map with a constrained internal linear combination (ILC) of the Planck frequency channels in the region around each cluster; the ILC is constrained to nullify the SZ signal from the clusters themselves and provide a clean CMB map of 5 arcmin resolution. Using a quadratic estimator on the CMB map, we reconstruct the lensing potential in the field and then filter it to obtain an estimate of the cluster mass. The filter is an NFW profile  (Navarro et al. 1997)  Fig. 2 : Cluster mass scale determined by CMB lensing. We show the ratio of cluster lensing mass, M lens , to the SZ mass proxy, M Yz , as a function of the mass proxy for clusters in the MMF3 2015 cosmology sample. The cluster mass is measured through lensing of CMB temperature anisotropies in the Planck data  (Melin & Bartlett 2014).
External data sets Cluster counts cannot constrain all pertinent cosmological parameters; they are most sensitive to Ω m and σ 8 , and when analysing the counts alone we must apply additional observational constraints as priors on other parameters. For this purpose, we adopt Big Bang nucleosynthesis (BBN) constraints from  Steigman (2008) , Ω b h 2 = 0. 022±0. 002 (for a recent review on BBN, see Olive 2013), and constraints from baryon acoustic oscillations (BAO). The latter combine the 6dF Galaxy Survey  (Beutler et al. 2011) , the SDSS Main Galaxy Sample  (Padmanabhan et al. 2012; Anderson et al. 2012 ) and the BOSS DR11  (Anderson et al. 2014). We refer the reader to Sect. 5. 2 in Planck Collaboration XIII (2015) for details of the combination. We also include a prior on n s from Planck Collaboration XVI (2014), n s = 0.
Comparison to 2013 We begin by verifying consistency with the results of Planck Collaboration XX (2014) (Sect. 6. 1) based on the onedimensional likelihood over the redshift distribution, dN/dz (Eq. 4). We then examine the effect of changing to the full twodimensional likelihood, dN/dzdq (Eq. 1) in Sect. 6. 2. For this purpose we compare constraints on the total matter density, Ω m , and the linear-theory amplitude of the density perturbations today, σ 8 , using the cluster counts in combination with external data and fixing the mass bias. The two-dimensional likelihood dN/dzdq is then adopted as the baseline in the rest of the paper. 6. 1. Constraints on Ω m and σ 8 : one-dimensional analysis Figure  3  presents constraints from the MMF3 cluster counts combined with the BAO and BBN priors of Sect.
5. 2; we refer to this data combination as "SZ+BAO+BBN". To compare to results from our 2013 analysis (the grey, filled ellipses), we use a one-dimensional likelihood based on Eq. (  4 ) over the redshift distribution and have adopted the reference scaling relation of 2013, i. e. , Eqs. (  7 ) and (  8 ), with the mass bias value fixed to 1b = 0. 8. For the present comparison, we use the updated BAO constraints discussed in Sect. 5. 2; these are stronger than the BAO constraints used in the 2013 analysis, and the grey contours shown here are consequently smaller than in Planck Collaboration XX (2014). Limiting the 2015 catalogue to q > 8. 5 produces a sample with 190 clusters, similar to the 2013 cosmology catalogue (189 objects). The two sets of constraints demonstrate good consistency, and they remain consistent while becoming tighter as we decrease the signal-to-noise threshold of the 2015 catalogue.
2, finding that neither the high nor the low redshift bin set prefers the steeper slope by itself; it appears only when analyzing all the bins. As described further in the Appendix, there is a subtle interplay between parameters that is masked by the degeneracies and difficult to interpret with the present data set. A related issue is the acceptability of the model fit. We define a generalized χ 2 measure of goodness-of-fit as Fig. 3 : Contours at 95 % for different signal-to-noise thresholds, q = 8. 5, 7, and 6, applied to the 2015 MMF3 cosmology sample for the SZ+BAO+BBN data set. The contours are compatible with the 2013 constraints (Planck Collaboration XX 2014), shown as the filled, light grey ellipses at 68 and 95 % (for the BAO and BBN priors of Sect 5. 2; see text). The 2015 catalogue thresholded at q > 8.
7 : Comparison of constraints from the CMB to those from the cluster counts in the (Ω m , σ 8 )-plane. The green, blue and violet contours give the cluster constraints (two-dimensional likelihood) at 68 and 95 % for the WtG, CCCP, and CMB lensing mass calibrations, respectively, as listed in Table  2. These constraints are obtained from the MMF3 catalogue with the SZ+BAO+BBN data set and α free (hence the SZα notation). Constraints from the Planck TT, TE, EE+lowP CMB likelihood (hereafter, Planck primary CMB) are shown as the dashed contours enclosing 68 and 95 % confidence regions (Planck Collaboration XIII 2015), while the grey shaded region also includes BAO. The red contours give results from a joint analysis of the cluster counts and the Planck lensing power spectrum (Planck Collaboration XV 2015), adopting our external priors on n s and Ω b h 2 with the mass bias parameter free and α constrained by the X-ray prior (hence the SZ notation without the subscript α).
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES , and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO ,  JA  and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which Article number, page 14 of 19 Planck Collaboration: Cosmology from SZ cluster counts technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planck-collaboration.
These data were collected through multi-sited election exit polls during the last Malian presidential elections, in July 2013, that is to say once democratic institutions have been restored after the coup of 2012. The survey was designed so as to have three teams of surveyors in France, Côte d'Ivoire, and Mali, all of them interviewing Malian voters exiting polling places the day of the first round of the elections. Among destination countries, France was chosen for being a competitive multiparty democracy, by contrast to Côte d'Ivoire which is generally considered as a defective democracy. 5 France and Côte d'Ivoire are also the main destination countries for Malian migrants, respectively in Europe and Africa. While the tradition of migration among the Malian population goes back to the precolonial period, the bulk of migration from Mali to Côte d'Ivoire and France occurred during the 1960s and 1970s due to the huge expansion of cocoa and coffee plantations in Côte d'Ivoire that need a lot of agricultural laborers, and to the demand of labor force of the manufacturing sector in France.
Moreover, the extent to which Malian migrants trust (or distrust) the institutions of their home country and the interest they have for Malian political life are found to depend on the country they reside in. The remainder of the article is structured as follows. In Section 2, we provide a brief overview of the literature on social remittances and discuss the existing evidence on the relationship between migration experience and migrants' political attitudes. In Section 3, we present the exit poll data collected in the framework of the POLECOMI project, and describe our empirical strategy. Section 4 presents the results and provides some suggestive evidence on the channels that could drive our results. Section 5 concludes.
Data and empirical strategy For the purpose of this research, we collected an original dataset on Malian voters interviewed in Mali, France, and Côte d'Ivoire during the first round of the last presidential election on  July 28, 2013. 7  To this end, we put in place multi-sited exit polls in different places: three suburbs of Paris (France), namely Bagnolet, Montreuil and Evry 8 ; three communes located in the Northern part of Abidjan (Côte d'Ivoire), namely Abobo, Adjamé and Attécoubé; three localities in the region of Kayes -Kayes, Kenieba and Kita; and the communes I, IV and VI of Bamako (Mali). 9 The choice of France and Côte d'Ivoire was driven by the fact that both are the main destination countries of Malian migrants, respectively in Europe and Africa. The focus on the capital cities and their suburbs was justified by the density of Malian migrants and polling stations in these localities, which facilitated the fieldwork and allowed us to interview large numbers of migrants in only one day.