dataesr/scientific-paragraphs-categorization huggingface dataset:
Empirical approach Framework for duration analysis As indicated above, our dataset contains a sample including both on-going outmigrants and return migrants, which allows us to analyse complete and incomplete length of migration. Migration duration data are right-censored by definition because the date of transition out of the state (i. e. , returning home) is unknown for on-going migrants. As highlighted by  Jenkins (2008) , survival (or duration) analysis offers a number of advantages compared with OLS or binary choice models for such data. In particular, it is well suited to account for the timing of migration events (including return migration), for censoring in the data, and for incorporating time-varying variables in the estimation. Because answers about migration duration were given in months, discrete time periods for migration duration are defined in months 15.
Endogeneity concerns One difficulty in investigating the role of children in the return migration decision is that there are potential identification concerns. First, migration plans and fertility may be simultaneous decisions. Second, unobserved factors that are related to fertility may simultaneously affect return plans. Unfortunately, our cross-section database does not allow us to adequately address the potential endogeneity bias from a statistical point of view. However, we argue that in the case of China, there are good reasons to believe that the implied bias should not be too strong because the fertility decision is not fully private and free as it is in other countries. That said, we also acknowledge that we cannot fully rule out potential identification issues and that the implied biases should be kept in mind when interpreting the coefficients.
(2012) , who find that for women, having a husband who has migrated increases the length of the last completed migration episode by eight months. One should note that the migration status of the spouse is likely to be endogenous because migration and the length of stay in city are household-level decisions. Hence, unobserved factors that are related to the spouse's decision to migrate may at the same time influence the migrants' return plans. However, the limitation of our cross-section dataset does not allow us to adequately instrument this variable. As a simple sensitivity check, Additional file 1: Table  A1  displays estimates of Model 3 without the migration status of the spouse (Model 3b) and confirms the stability of our estimates. Finally, the effect of having elderly above the age of 70 in the household is negligible.
See  Gong et al. (2008)  for a comparison of all survey data available for China. 11 The correlation coefficients are -0. 65 for the whole population, -0. 95 for outmigrants, and -0. 52 for return migrants. All the correlations are statistically significant at 1 per cent. 12 All children-related variables are computed at the moment of return for return migrants and at the time of the survey (2008) for on-going migrants. 13 Considering the household, rather than the individual, as the most appropriate decision-making unit in return migration is consistent with the "New Economics of Labor Migration" (NELM) literature, which explicitly integrates migration decisions into a household strategy  (Taylor, 1999). 14 See, e. g. ,  Lee (2008)  for a review of the long history of pro-son bias in China.
Acknowledgement We gratefully acknowledge the financial support of the  Région Rhône-Alpes (France)  and the " Household Registration System Reform and Urban Population Management Innovation Study "  Project of the Center for Modern Chinese City Studies (CCMC ) at the  East China Normal University. We are grateful to  Ding Jihong  for his precious help and advice, to  Sun Zhongfeng  and  Anhui University  for the helpful assistance on the survey, and to Wuwei local officials for their support and help during the fieldwork. We thank  Tor Eriksson ,  Marc Gurgand ,  Hubert Jayet ,  Guy Lacroix ,  Yang Chengyu  and a referee for their useful comments on earlier versions of the paper. Any remaining mistakes are our own. Responsible editor: Amelie Constant.
Study design and participants We analyzed cross-sectional data from participants in the NutriNet-Santé Study, a web-based prospective observational cohort launched in France in 2009, focusing on the relationship between nutrition and chronic disease risks as well as the determinants of dietary behaviors. Volunteers aged 18 years or older (age range 18-96 years) living in France and having access to the Internet fill in self-administered web-based questionnaires at baseline and then regularly during follow-up using a dedicated, secure website. A detailed description of the NutriNet-Santé study has been published previously  [23]. Participants in the present study were subjects from the NutriNet-Santé cohort who completed a questionnaire on physical activity and mobility, administered from February 15 to August 15 2013 (n = 55,694; 48.
5 % participation rate). This questionnaire was designed to assess active transport in everyday life over the past four weeks. From the sample who filled in the physical activity questionnaire, 1730 participants were excluded because of physical limitations to mobility, such as self-reported motor impairments (n = 927) or self-reported limitations to walking (item ' Ability to walk 100 m' n = 803). Additionally we excluded participants who were pregnant (n = 730), reported implausible physical activity values (n = 2817), or had missing data regarding the covariates used in multivariable analyses (n = 11,122). Thus, we reached a final sample of 39,295 subjects with a mean ± SD age of 49. 1 ± 14. 4 years.
The validity and reliability of the specific questions on travel-related activities have been assessed in 88 subjects aged 20-65 years (article under revision). Briefly, the estimate of active transport time was found significantly correlated with data obtained by a logbook (r = 0. 40, mean bias 7. 2 %), and reliability was moderate (intra-class coefficient 0. 47 for 1-month test-retest). The travel questions were detailed by type of transportation (car, public transportation, walking, cycling, and other mechanical vehicle) and included the mean number of days per week and the mean duration per day where the particular type of transportation was used. For each type of transportation, results were expressed in h/week. Walking and cycling by domain were dichotomized (≥0. 5 h/week and ≥ 0 h/week, respectively).
Covariates Individual and socio-demographic variables were assessed by self-administered questionnaire completed by participants at inclusion. Data included age, gender, weight and height, educational level (more or less than 2 years of university), household income (0-1,430 Euros/month, 1,430-2,330 Euros/month, 2,330-3,780 Euros/month, more than 3,780 Euros/month, do not know/do not want to respond), smoking status (yes or no), household composition (living alone or in a couple), presence of children at home (aged under 13 years, between 14 and 18 years), selfrated health (poor to average, good to very good) and home address. Age was categorized by 5-year age group for figures and used continuously in other analyses. Body mass index (BMI) was calculated as reported weight (kg) divided by reported height squared (m 2 ).
The sum of all the mean durations per week of these activities was categorized as between 0 and ≤ 2 h per day, between 2 h and 4 h per day and more than 4 h per day. City density (number of inhabitants/surface) was obtained from the Census databases (www. insee. fr) and categorized as follows: 0-300 people per km 2 (rural area), 300-2000 people per km 2 and more than 2000 people per km 2 (high density city).
Whereas objective measures could provide more accurate data on activity patterns, subjective measures remain important because they provide domainspecific information  [41]. Physical activity and travel behavior over the past four weeks were assessed over a period of 6 months and we did not take into account possible seasonal variation. Our sample included proportionally more women and more individuals of high educational levels, as observed in general in volunteer-based studies  [42]  and participant behaviors were only assessed during half a year. Moreover, validation of the questionnaire was performed in a population aged from 20 to 65 years, which is different from our population study (age range 18-98 years), and domestic physical activities were assessed by only one single question. For these reasons extrapolation of these findings must be done cautiously.
Acknowledgements This work is part of the  ACTI-Cités  project (coordinator: JM Oppert) carried out with financial support from the  French National Cancer Institute (Institut National du Cancer, INCa)  through the Social sciences and humanities and public health programme (2011-1-PL-SHS-10). The NutriNet-Santé cohort study is funded by the following public institutions:  Ministère de la Santé, Institut de Veille Sanitaire (InVS) ,  Institut National de la Prévention et de l'Education pour la Santé (INPES) ,  Fondation pour la Recherche Médicale (FRM) ,  Institut National de la Santé et de la Recherche Médicale ,  Institut National de la Recherche Agronomique (INRA) ,  Conservatoire National des Arts et Métiers  (CNAM) and  Paris 13 University. Menai Mehdi  was funded by a grant from  Fondation Le Roch -Les Mousquetaires.
The funding sources played no role in the design and conduct of the study; the collection, management, analysis and interpretation of the data; or the preparation, review, and approval of the manuscript by coauthors. The authors thank  Younes Esseddik ,  Paul Flanzy ,  Mohand Ait Oufella ,  Yasmina Chelghoum , and  Than Duong Van  (computer scientists),  Florence Charpentier  (dietitian),  Elise Martin  (communication assistant),  Nathalie Arnault ,  Véronique Gourlet ,  Fabien Szabo ,  Laurent Bourhis , and  Stephen Besseau  (statisticians), and  Rachida Mehroug  (logistics assistant) for their technical contribution to the  NutriNet-Santé study. Study registration: The  NutriNet-santé study  was registered under the EudraCT number  2013-000929-31.
Women living in highly educated communities have higher odds of using contraceptives in general, but not artificial methods, whereas women living in highly religious communities have lower odds of artificial method use, but are not less likely to have used contraceptives in general. Our data come from interviews with 1995 randomly selected women (aged 18-91) living in 22 randomly selected communities in the midst of a demographic and subsistence transition in southern Poland (electronic supplementary material). The data capture a period of profound social and economic change in one of the few European regions where subsistence farming is still practised and where fertility and contraceptive use are highly varied. The study region has a long history of peasant agriculture, with more than 65% (1255) of respondents living in households subsisting partly or mainly from their own produce.
Material and methods Data were collected between 2009 and 2010 as part of an anthropological -demographic project. All variables are described in the electronic supplementary material. Education and religiosity were group-mean centred so that community means did not introduce confounding colinearity. We exclude network density from our SN model, as this removes women with only one or no network partners. Electronic supplementary material analysis reveals that model outcomes do not differ when including (i) network density (electronic supplementary material, table  S7 ) or (ii) mean network education (also reducing sample size, electronic supplementary material, table  S8 ). All data were analysed in R v. 3. 1. 1 [62]. Ethics statement. All participants gave informed consent and the study protocols were approved by the Ethics Committee of the Department of Anthropology at UCL.
Acknowledgements. We are indebted to all of the women who took part in our study. Grazyna Jasienska ,  Ilona Nenko  and  Andrzej Galbarczyk  provided enormous support in setting up the project, and we thank our field assistants, the  Wojtas  and  Markiewicz  families, and the parish rectors who helped to publicize the study. Adam Powell  and two anonymous reviewers provided insightful comments on the manuscript.
Acknowledgments The authors are grateful to  Joeni Setijo Rahaju  and  Deby Arifiani  at the  Herbarium Bogoriense (BO)  for their support and assistance, and to  PT Weda Bay Nickel  for the opportunity to participate in their Flora Inventory Programme on Halmahera, with special thanks to  Tjut Fatisa Bangun ,  Bahar Fabanyo ,  Iska Gushilman ,  Idris Haris ,  Samsul Lasut ,  Gavin Lee ,  Roji Mahroji ,  Edi Permana , and  Bilal Sau. We want to sincerely thank  Ehoarn Bidault ,  Susana Arias Guerrero ,  Pete Lowry ,  Mary Merello  and  Ed de Vogel  for their interest, tireless support and help in this study. We also thank  Roy Gereau  for preparing the Latin diagnoses. We are finally grateful for the valuable comments on the original manuscript provided by  Henk Beentje.
Recently, we described two modelling approaches that can accommodate some of these issuesmulti-state models and joint latent class mixed models  [13]. Here we examine four markers of lifestyle activity and self-perception of social relationships (social, physical and intellectual engagement, social network size, satisfaction with social relationships, and perceived understanding) with longitudinal cognitive change and risk of dementia. A previous analysis on the same dataset combined these factors to create an overall index that was related to cognitive ability but not decline in the six psychometric tests examined  [12]. The individual facets have also been modelled against risk of dementia  [14]. However, the variables have not been studied in isolation with respect to cognitive decline, and using advanced modelling methods that include multivariate repeated cognitive information and consider risk of dementia alongside decline.
Study population Data are from the Paquid cohort, which is a longitudinal study of ageing with up to 20 years of follow-up  [15]. Recruitment of 3777 participants occurred across 75 civil parishes of the Gironde and Dordogne regions of south west France. Subjects were aged 65 years and above, residing at home at the study baseline in 1988. There have been up to nine subsequent waves of data collected on participating individuals at 1, 3, 5, 8, 10, 13, 15, 17, and 20 years after the baseline assessment. At the first follow-up, only subjects from Gironde were interviewed.
Longitudinal cognitive change and dementia risk Initially, cognitive decline and time to dementia were modelled independently. Evolution of the latent general cognitive factor underlying repeated measures of six cognitive tests was assessed using a linear mixed model with a quadratic age trend to account for non-linear mean cognitive decline  [21]. Age (re-scaled by subtracting 65 years from all data points) was used as the time scale for the model. Given that a previous longitudinal analysis of cognitive decline in the Paquid cohort found no evidence for cohort effects  [13] , age at baseline was not included as a covariate. Random-effects were included to account for the individual variation in the intercept and both the linear and quadratic slopes of decline. Education and sex were considered as covariates.
Distribution of the lifestyle activity and self-perception variables across these classes were compared a posteriori using chi-squared tests for the categorical variables and analysis of variance (ANOVA) for the continuous measures. Data were analysed using a Fortran90 programme (http://www. isped. u-bordeaux. fr/BIOSTAT) developed by Proust-Lima et al. [23]  and in R  [24]  using the survival package  [25].
Acknowledgements Methodological developments for this work were partly funded by the  ANR -funded project  MOBIDYQ ; grant n°:  2010 PRSP 006 01. PAQUID study was funded by  NOVARTIS pharma ,  IPSEN, Agrica, Conseil régional d'Aquitaine, Caisse Nationale de Solidarité et d'Autonomie. Fiona Matthews  is funded under  MRC  programme grant  UC_US_A030_0031. Riccardo Marioni  is an Alzheimer's  Research UK Fellow  ( ART-RF2010-2 ). Riccardo Marioni  is a member of  The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology  ( MR/K026992/1 ), part of the cross council  Lifelong Health  and  Wellbeing Initiative. Funding from the  BBSRC  and  MRC  is gratefully acknowledged.
Acknowledgements We thank  Operational Branch Mobility and Waterways of Wallonia Public Service (Department of Economic and Environmental Impacts)  for allowing us to sample dreissenids in canals during lock maintenance. We would also like to thank the  Voies Navigables de France (VNF) , and especially  Frédéric Rogissart , for authorization to work on the Givet dam; and the  Flemish Environment Agency (VMM)  for providing us with data and allowing us to examine their collection. We also thank  Paul Jacobs  for providing us with many of the samples. We thank the three anonymous reviewers and the editor for their suggestions, which greatly improved an early version of this manuscript. This study received financial support from the  University of Namur , the  European Fisheries Fund  ( FEP 32-1109-004 ), the  Walloon Region  and from ' European Cooperation in Science and Technology'  ( COST Action   TD1209 ).
Acknowledgments This study was partially funded by the  French Health Ministry (Projet de Recherche en Qualité Hospitalière   2009-PREQHOS 2009 ) and by the  Fondation Méderic Alzheimer  project grants  2012-2014. The authors thank all professionals of the  Gerontology Department  who contributed to the recruitment (among others,  Marie-Laure Seux ,  Catherine Bayle ,  Emmanuelle Duron ,  Hermine Lenoir ,  Olivier Hanon ,  Matthieu Plichart ,  Sandra Boepsflug ,  Sophie Chauvelier ,  Jocelyne de Rotrou ,  Florian Labourée , and  Souad Damnée ). We acknowledge  Marie Godard  and  Cécile Klochendler  for her methodological advice and  Valérie Belrose  and  Déborah Rechard  who monitored this study (from  Clinical Research Unit of Cochin Hospital-Unité de Recherche Clinique/Centre d'Investigation Clinique URC/CIC Necker Cochin  under the direction of  Prof Jean-Marc Tréluyer ).
Country selection For the purposes of this research, we adopted an economic and wealth-related definition to categorize developed and developing countries  (World Bank, 2010). We used this data to identify France, the United Kingdom and the United States of America as being the developed countries in our sample. We used the same data to categorize developing countries. Given that it is not possible to survey every developing country, we decided on two criteria: first, that we would include the two largest developing countries -China and India -and second that we would seek developing countries from every major continent. Thus, we have Turkey and Egypt from Europe/Middle East, two African countries -one small and one large, another mid-size from South East Asia and the largest country by population from South America.
Sample We have used a sample of younger generation consumers in this research, age range from 18 to 45 years old. The reason is that the purpose of this research is to explore some aspects of globalization and this age group is most likely to be amenable to this topic and have the lowest barriers to international trade  (Shukla, 2011). In addition, this segment contains hundreds of millions of people globally, and is particularly interesting to managers of multinational firms  (Strizhakova et al. , 2008). Survey data for this research was then obtained from 2655 respondents in eleven countries. The developed countries were the USA (n = 345), France (n = 214) and the UK (n = 213). The developing countries were China (n = 264), Egypt (n = 261), India (n = 333), Brazil (n = 94), Malaysia (n = 304), Mauritius (n = 59), South Africa (n = 224) and Turkey (n = 345).
Hypothesis testing To test the proposed hypotheses, we conducted multiple group analysis on the two sub-data sets (developing country sample versus developed country sample) following the procedure recommended by  Byrne (2010)  for moderation variables. The results of the test are presented in Table  6. The first part of all hypotheses was tested using path coefficient analysis. The second part of the four hypotheses required testing the differences in the magnitude of effects between consumers from developed and developing countries. We used a series of comparisons between the unconstrained model and models in which only one structural path at a time was set to be equal between the two sample sets. Age, gender, and household income were used as control variables. It can be seen from Table  6  that H1 is partially supported, indicating that cosmopolitanism is positively related to home product country image for developed countries (Beta =.
Acknowledgements The authors would like to thank  Prof. Richard Croucher  and  Prof. Marina Papanastasiou  at  Middlesex University ,  Prof. Kamel Mellahi  at  Warwick University, Dr Georgios Chrysochoidis at University of East Anglia , and  Prof. David Ralston  at  Florida International University  for their helpful comments and support for an earlier version of the paper. We would also like to thank  Prof. O  ¨zlem O ¨zkanlı at  Ankara University ,  Mr. Paul Garneau  and  Dr. Yi Zhu  at  Middlesex University  for their back translation of the Turkish, French, and Chinese versions of the questionnaire respectively. The authors are also grateful to the three anonymous reviewers for their comments on an earlier version of this paper.
Socioeconomic status Socioeconomic status drivers are among the most prominently measured characteristics in social vulnerability studies, and manifest in different ways across levels of geography. Common socioeconomic status indicators include measurements of household income, poverty, unemployment, educational status, wealth, inequality and home value. At the individual level, lack of resources, power relationships, poverty, and marginalization translate into social vulnerability through access to resources, coping behavior and stress  [2]. At the community level, social vulnerability is determined by relative distribution of income, access to resources, and diversity of economic assets  [45]. Table  4  highlights findings from key studies involving socioeconomic characteristics; detailed citation frequency data are provided in Table  A2.
g. , individual, network) different from other indicators. Indicators for these aspects typically cannot be computed from publicly available databases (e. g. , national censuses) and require the use of qualitative methods, targeted surveys, and participatory approaches. To address this caveat, scorecards have become a popular survey mechanism, especially for researchers interested in understanding urban resilience (see  [94] ,  [75] ,  [96] ). Research is needed, however, to better integrate the findings of studies employing such methods. In particular, the potential of participatory approaches to generate salient quantitative data is still underestimated  [68]. Measurability is also constrained by limited understanding of underlying social vulnerability processes. For example, being a child  [100, 61] , an elder  [77, 95] , a woman  [36, 90]  and a member of a minority  [28, 98]  were protective factors in some studies.