dataesr/scientific-paragraphs-categorization huggingface dataset:
Study protocol The study design of the whole FLAM study has been fully described elsewhere  [27]. Briefly, FLAM is a randomized controlled trial assessing the effect of FV vouchers in low-income families over a one-year period, through a pre-test post-test design. In this first work, a mixed methodology was used. Collected data were described through usual quantitative methods, and this analysis was completed by a qualitative survey aiming to understand the reasons for non-participation.
Questionnaires and data collection Data were collected via face-to-face questionnaires administered by trained interviewers, for about 1 h duration. Volunteer families were interviewed at community centres, or at home in order to sign the consent form, and complete the questionnaires. Children older than 5 years were directly interviewed, whereas food consumptions were estimated by the parent for younger children. Data on inclusion criteria and sociodemographic characteristics were first collected. Then, a food frequency questionnaire was used to describe the consumption of children and adults in 13 main food groups (as cereal products, starches, vegetables, fruits, legumes, dairy products, meats and eggs, fish and sea-food products, fast-food and pizza, salty snacks, sweet products, and beverages).
This allowed assessing the frequency of their specific daily, weekly or monthly intakes (the portion sizes were not assessed). Participants were considered low consumer of FV when they reported eating less than 3. 5 servings of FV per day  [35]. This questionnaire were adapted from those used for the ABENA study, which were specifically designed to be administered to disadvantaged groups  [8]. Finally, information on living conditions and financial difficulties was also collected. We relied upon the latter data and sociodemographic characteristics to compute the EPICES score  [36]. Based on 11 questions on various socioeconomic determinants, this individual score assesses the precariousness level of subjects. It ranges from 0 (the less precarious situation) to 100 (the most precarious situation), with a threshold of 30.
Qualitative analysis The qualitative survey was conducted from March to September 2017. It was performed through semi-directive telephone interviews among people who refused to participate or did not show-up to inclusion meetings. Participants were recruited on a voluntary basis. The number of interviews was determined using the data saturation method, based on the assumption that the new collected data do not bring any new element to understand the phenomenon under study. The data were collected with audio recording before being transcribed and anonymized. All interviews were analysed through thematic content analysis. For each of them, a first intuitive reading, called "floating" reading, allowed the emergence of the main ideas of the speeches  [37]. A second reading called "in-depth" was done, focusing on the meaning of the discourses  [38].
Acknowledgements Claudia Chahine  and  Cynthia Perlin dieticians , for their precious participation to this study and the elaboration and conduction of workshops during the all duration of the study. The FLAM interviewers:  Laëtitia Defoi ,  Anita Houeto ,  Anouchka Kponou ,  Lysa Tagherset  and  Marion Genest. The authors thank for their welcome and their help the community centres of  Saint-Denis  ( Pierre Sémard ,  Romain Rolland , Floréal), the Municipality health care centres ( CMS du Cygne, CMS Henri Barbusse ,.
In this article we analyze the relationship between the loan portfolio quality and the soundness of the banking system in the Central African Economic and Monetary Community. The data used concern a panel of 41 banks in 4 countries in the Community for the period 2000 to 2013. For the analysis of the influence of different indicators, we use a model in nested equations. This study shows that taking into account both individual and macroeconomic indicators makes it possible to neutralize the effects of the deterioration of the quality of the credit portfolio on bank soundness. These indicators together improve the contribution of intermediation activity to the strength of banks.
Some authors, such as Demirguc-Kunt and Detragiache (1998),  Goyeau and Tarazi (1992)  and  Powo (2000) , show that these individual indicators of robustness may offer limited information on the shock-resistance capacity of the banking system. Studies on risk management by banks can be grouped into two categories. One category uses individual data and the other uses macroeconomic data. Demirgüç-Kunt and Detragiache (1998), using macroeconomic data, concluded that both in developing and developed countries, the unfavorable macroeconomic environment (low GDP growth and / or high inflation rate) negatively affects the quality of the credit portfolio and the profitability of banks. Thus, the macroeconomic environment can precipitate the banking system in a situation of distress. Hardy and Pazarbasioglu (1998)  come to the same conclusion using a Logit model on Asian banks.
On the other hand,  Diamond and Dybvig (1983)  study on the rush to the counters highlighted the dominant role of bank-specific factors, thus minimizing the effect of the macroeconomic environment. Goyeau and Tarazi (1992) , on the other hand, looking at the difficulties of banks in Europe, underline the crucial role of the insufficient coverage of risks by the banks as factor of vulnerability of these institutions. For Africa,  Powo (2000)  uses a multivariate Logit model to assess the probabilities of bank survival and the advent of a banking crisis in the UEMOA zone. Using data from the banks 'balance sheets, he finds that the elements related to the composition of the banks' portfolio (such as commercial paper) and the small amount of term deposits were decisive in the bankruptcy of banks during the second half of the 1980s.
This leads us to adopt the GMM as a system for estimating our model. In summary, the model which we wish to estimate is written in the form of the following system of equations:  1 1 it it it t it it it it t i it Z Z X M Z Z X M                          (3) The variables used are a mixture of variables from the empirical literature and ratios that we consider to be relevant for the management of banks in the CEMAC. For the development of the model, we have panel data for 41 banks out of 50 CEMAC banks for the period 2000 to 2013.
The last group is that of the variables that rely on capital. The Shareholders' Equity / Total Assets (FPTA) is an indicator of financial effect. This is another measure of the capital adequacy of banks' credit portfolio. It was also the ratio Shareholders' equity / Total Loans (FPTCR). The latter represents, according to  Godlewski (2004) , a buffer to absorb the potential losses. As one of the main activities of the bank is the granting of credit, we expect these two variables to have a positive impact on the bank's soundness. For the study of the financial health of the bank, one must take into account its environment. To do this, we retain certain macroeconomic variables that represent the environment for us. Given the lack of adequate data and the scarcity of analyzes on the CEMAC banking sector, we retain the GDP growth rate (GDP) and the consumer price index (CPI) as macroeconomic variables  (Kadandji et al.
The Quality of the Data and the Processing of Accounting Information Studies of vulnerability, fragility, stability and soundness of financial institutions use macroeconomic and financial data. These data may come from the bases used by specialized institutions such as the central bank, the regulatory body, the financial market, etc. For this study, we use annual data, often used in the empirical literature. The statistical data used to conduct our analyzes are taken from the CERBER (Note 6) system used by the General Secretariat of the Central African Banking Commission (COBAC) and the annual reports of the central bank. This system allows the supervisory body, after the financial statements are transmitted by the banks, to restore their financial situation. This is the main piece-based control tool.
The data collected for the period 2000 to 2013 relate to the annual balance sheets and annual profit and loss accounts of 41 banks located in Cameroon, Congo, Gabon and Chad. For calculating the ratios relating to the balance sheet and profit and loss account, we only work with the annual data retained by COBAC on 31 December of each year. The macroeconomic data used relate to the countries in which the banks in our sample are located. Given the structure of our model and our data, we used the statistical and econometric software Stata / SE 12. 0, to estimate our equations. The estimates produced results that should be presented.
The quality of this model can be improved by integrating cultural and institutional variables. This is only possible if the barrier to full data access on the CEMAC banks is lifted. It is for this reason that Soupmo Badjio (2009) points out the danger posed by the lack of financial communication in CEMAC, for the development of research and the banking sector in this subregion. In addition, the IMF and the World Bank evaluate the financial sectors of the CEMAC countries to find that there are reforms to improve the financial sector in this area. Our study is similar to that of Gulde-Wolf and Ghura (2013), who advocate to mitigate the risks that threaten the stability of the CEMAC financial sector, strengthening the regional regulatory authority, strict enforcement of prudential standards and the consideration of macroeconomic indicators in the determination of rules.
Results In their systematic review using clinical trial data published up to late November 2017, Conforti et al. assess the relationship between gender and efficacy of checkpoint inhibitors versus standard of care (primarily chemotherapy). Their dataset uses 20 randomized, controlled trials covering several malignancies and clinical indications, with melanoma and non-small cell lung cancer trials being the major contributors to the dataset. They then calculated pooled hazard ratios in each gender versus their control, standard of care group. The data from this analysis suggests that men had significantly reduced risk of death (HR =0. 72, 95% CI, 0. 65-0. 79) versus standard of care groups than women.
Evaluating the evidence Although the methodology and high-quality dataset used by the authors should be lauded, this study fails to immediately inform our understanding of immunotherapeutic agents and their clinical use because of the absence of several data points. Most importantly, the authors did not investigate the relationship between PD-L1 expression or tumor mutational burden (TMB) and gender. Differences in these parameters are a potential explanation for this data, and this analysis will be absolutely necessary. In assessing PD-L1 expression/TMB and gender, there are two possibilities: either PD-L1 expression/ TMB is significantly higher/lower in men thereby contributing to the results seen here, or there are no significant differences in PD-L1 expression/TMB among men and women. Given the higher efficacy of checkpoint blockade seen in men in this study, it is likely that if PD-L1 expression/TMB is different, men would have higher levels.
recruitment of participants Each Örenäs Research Group local lead was asked to email an invitation to take part in the survey to the PCPs in their local health district, and to recruit at least 50 participants. In six countries (Denmark, Norway, Portugal, Romania, Slovenia, Sweden), the invitation was distributed to a national sample. Any local leads who had difficulty in achieving the required sample sizes were asked to increase the number of responses by using snowballing. 33 Consent was implied by agreeing to take part in the survey. All data were collected anonymously.
Health system item Component Factor Open access Open access most interest in this subject were the most likely to respond. However, while this selection bias may have affected the factor loadings, it is unlikely to have changed the factor structure itself. While the demographic data that we collected included the gender of participants and the number of years that they had been in practice, we have found no equivalent data on national PCP populations that would allow us to assess how representative our samples were. Most samples were taken from each local lead's own locality, and these may not have been representative of their nations as a whole. 36 While this makes it difficult to generalise the findings to each country, the variation between countries is relevant and valid. The recruitment method used in this study resulted in variable response rates, leading to a risk of non-response bias and loss of power.
Based on a new dataset of occupations, we will provide for the first time a macroeconomic overview of specialisation patterns in the global economy. This contributes to a better understanding of the interplay of comparative advantages across regions, speaking to theories of multinational production and international trade  (Markusen, 2002) , urban development  (Duranton and Puga, 2005)  and global production systems  (Gereffi et al. , 2005; Coe et al. , 2008). The traditional approach to measure specialisation in trade is based on the product composition of countries' gross export flows. The shortcomings of this approach became increasingly apparent as international production fragmentation progressed. Based on gross exports numbers, one could, for example, find that the Philippines and China had a strong comparative advantage in high-tech products, such as electronics, at the end of the 1990s, while Japan and the USA did not  (Lall, 2000).
Interestingly, Austria appears to have a comparative advantage in R&D activities although neither gross exports, nor value added based indices revealed a comparative advantage. This example illustrates that the FS approach is not only conceptually appealing, but can also be fruitfully applied in empirical work and is able to uncover new patterns of specialisation. This is our main point, elaborated upon in the remainder of this article. We proceed as follows. Section 2 briefly reviews the main findings from the first and second generation trade statistics on product and vertical specialisation in exporting. Based on a discussion of their limitations, we plea for shifting attention to FS. In Section 3, we lay out our methodology to trace FS in exports, and how to bring it to the data. We collected administrative data on occupational employment and wages, primarily from labour force surveys, for 35 industries in 40 countries.
The new dataset is Functional specialisation in trade. 3 made public such that subsequent work is encouraged. This information is harmonised with information on inter-industry and inter-country flows of goods and services as recorded in the World Input-Output Database (the WIOD, see  Timmer et al. , 2015). Section 4 provides a discussion of empirical results on FS in the global economy, based on a variant of the  Balassa (1965)  specialisation index. We find a strongly positive correlation between GDP per capita and export specialisation in R&D functions, and a strongly negative correlation for fabrication functions. This reveals deepening of FS in the world economy. For management and marketing functions, correlations with GDP per capita are weak, however. Moreover, heterogeneity in specialisation patterns is large across countries at similar levels of income, suggestive of idiosyncratic factors that determine the local development of functions.
Based on cross-classified data (occupation by educational attainment level) from the American Community Survey 2007 we find that the share of college educated workers varies from a low 0. 07 in fabrication activities, 0. 27 in marketing, 0. 54 in management and up to 0. 67 in R&D activities carried out in the USA. Further research into the mapping of factor inputs into functions is needed to relate FS to countries' factor endowments and remuneration. We built up a new Occupations Database from detailed survey and census data for forty countries, including all EU 27 countries (as of January 2011), Australia, Brazil, Canada, China, India, Indonesia, Japan, Mexico, Russia, South Korea, Taiwan, Turkey and the USA. For each country, data is collected for 35 industries (this provides information for our matrix B).
These include agriculture, mining, construction, utilities, fourteen manufacturing industries, telecom, finance, business services, personal services, eight trade and transport services industries and three public services industries. For each country-industry, we have collected time series information on occupations of workers and their wages. For the USA, we use the same data sources as in  Autor (2015) , namely the 2000 Current Population Census and the annual American Community Surveys. Data for European countries are from the harmonised individual level European Union Labour Force Surveys. For the remaining countries we obtain the required data from national statistical offices. For example, for China we obtain occupations data from the population censuses. We find large variation in the occupational distribution of workers in the same industry across different countries.
For China, we obtain wage data from the IZA Wage indicator surveys. Other countries provide wage and employment information in the same data source. To apply our methodology we need further information on exports (e), industry output and value added (V) and domestic intermediate requirements (A D ) for each country. 12 Annual data for these variables is taken from the 2013 release of the WIOD which has a similar industry classification as the occupations database. The WIOD is constructed using the conceptual framework of the System of National Accounts and on the basis of published national accounts and international trade statistics, as discussed in  Dietzenbacher et al. (2013)  and  Timmer et al. (2015). As such it takes into account reported nominal flows of goods and services between plants within and across countries.
Its FS index Advanced countries include Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Luxembourg, The Netherlands, Portugal, South-Korea, Spain, Sweden, Taiwan, UK and the USA. Calculated according to Equation (1), comparing functional income shares in exports of all goods and services by all advanced countries to the same shares for all countries in the world. Source: Authors' calculations based on World Input-Output Database (November 2013 release) and Occupations database. for fabrication was already high in 1999, increased even somewhat further after 2001 when it joined the WTO, and levelled off after 2007 (Figure  2 ). The income share in exports from fabrication activities in China is estimated to be about 63. 4% in 2011, with an FS index of 1.
We use this data to measure FS of provinces in the production for overall Chinese exports. 20 rovinces can contribute to Chinese exports through exporting internationally themselves, or through delivering intermediates to other provinces that subsequently export internationally. The FS indices are calculated according to a modified version of Equation (1). The numerator now measures the share of function k in the overall income in province i that is related to Chinese exports. The denominator is the same share for all provinces together. If the index is above one, the province is said to be specialised in that function in the domestic production network of Chinese exports. The first column in Table  3  shows the provincial shares in domestic value added in Chinese exports for the year 2012.
Concluding remarks In this article, we argued the need for analysing FS of regions in global production networks, and demonstrated the potential for its measurement. We proposed a novel index based on comparing occupational labour incomes. This index is grounded in the new reality that countries and regions are competing in tasks rather than products. Countries export a bundle of activities, some of which are carried out locally and others imported. This insight has been around for some time, and patterns of vertical specialisation in trade have been documented using trade-in-value-added statistics. Yet, until now we lacked data on the type of activities that were carried out. We collected new rich data on the occupational structure of the labour force to characterise domestic activities that add value in exports.
In the short run this involves mixing existing firm-level data and firm characteristics (such as firm size, ownership and export status). In the longer term this would entail the development of common business registers across countries, increased data reconciliation and new data collections on value-chains beyond counterparty transactions  (Landefeld, 2015). The measurement of trade in (and use of) intangibles, in particular intellectual property such as brand names, databases and designs is a particular area in need of further research. Promising is the development of new firm-level surveys that track the type of business functions that are carried out domestically and those that are offshored by a multinational firm within GVCs. These surveys are in a testing phase and may become part of a regular statistical program  (Nielsen, 2018 ).
Notes: RD refers to R&D; MGT to management; MAR to marketing and FAB to fabrication. Allocation of countries to a particular group is based on highest functional specialisation index of the country in 2011. Entries bigger than one in bold. Source: Authors' calculations according to Equation (1) based on World Input-Output Database (November 2013 release) and Occupations database. Functional specialisation in trade. 29 Downloaded from https://academic. oup. com/joeg/article-abstract/19/1/1/5210032 by University of Groningen user on 05 February 2019 Bilateral functional specialisation of China and Germany in 40 destination markets, 2011 RD refers to R&D; MGT to management; MAR to marketing and FAB to fabrication. Entries bigger than one in bold. The indices in the final row correspond with the unilateral indices in Table A1.
Acknowledgements We are grateful to the editors of the journal, in particular  Harald Bathelt  and  Fre´de´ric Robert-Nicoud , as well as several anonymous referees, for insightful and stimulating comments on earlier versions of this paper. We would also like to thank  Richard Baldwin ,  Bart Los  and many seminar participants at the  University of Oldenburg, IDE Jetro , the OECD, UNCTAD, EMAEE 2015 Maastricht, IIOA 2016 Seoul and  CompNet Rome  for stimulating discussions.
Source: Authors' calculations according to Equation (2) based on World Input-Output Database (November 2013 release) and Occupations database. Germany China RD MGT MAR FAB RD MGT MAR FAB Australia 1. 51 0. 95 0. 96 0. 82 0. 50 0. 31 0. 89 1. 66 Austria Specialised in R&D Czech republic Belgium Denmark Bulgaria Finland Brazil France Canada Germany China Sweden Cyprus Taiwan Czech Republic Australia Germany Belgium Denmark Estonia Spain Greece Estonia India Finland Ireland France Latvia UK Lithuania Greece Malta Hungary Netherlands Indonesia Slovenia India Spain Ireland UK Italy US Japan South Korea Specialised in marketing 1. 17 1. 18 1. 22 1. 30 1. 42 1. 48 1. 42 1. 53 1. 59 1. 42 1. 45 1. 34 1. 43 1. 63 1. 27 1. 07 -1. 20 1. 21 0. 81 1. 33 0. 92 1. 30 0. 79 1. 25 0. 97 1. 35 0. 65 1. 35 0.