dataesr/scientific-paragraphs-categorization huggingface dataset:
Insert Table 3 here Given that data were collected from France, Hungary, and the U. K. , it was necessary to assess the cross-cultural measure equivalence across these three countries. Such an assessment would ensure the meaningfulness of our cross-sectional perceptual data gathered in different countries. To this end, we followed a sequential process of estimating and comparing nested models to assess: first, configural invariance, testing if the factorial structure of the constructs (i. e. , number of factors and observed indicators of each factor) is similar across the three samples; and, second, metric (factor loadings) invariance, testing whether the strength of association between the observed indicators and their underlying constructs is equivalent across the samples and thus establishing whether each construct has the same meaning across samples  (Steenkamp & Baumgartner, 1998; Swoboda & Hirschmann, 2016).
g. ,  Griffith & Lee, 2016; Zeugner-Roth, Zabkar, & Diamantopoulos, 2015). In each of the three samples, we identified the secondsmallest correlation and adjusted the correlation matrix using this value  (Lindell & Whitney, 2001; Malhotra et al. , 2006). In all three datasets, we observed no major differences between the original and the adjusted correlation matrices, and no significant correlation coefficient was reduced to non-significant levels (for p < 0. 05, two-tailed). This evidence suggests that common method bias is not likely to affect our study findings in any of the three countries.
In the U. K. , offering technical service support to buying firms was uniformly emphasized in the customer relationship management practices of supplying firms. This is the case among Hungarian suppliers with the provision of complaint handling service to their customers. Although the mean score for supplier performance in this service was the highest among Hungarian firms compared to French and U. K. firms, field interviews in Hungary indicated that business customers do not perceive that such a service is as critical as supplier performance in the other operational areas. Likewise, trust's impact on loyalty is consistent among smaller firms, but this effect differs among larger companies across the datasets: while among U. K. buying firms trust is important, this is not the case for organizational customers in France and Hungary.
Acknowledgements This article and the accompanying special issue are part of the  INVALUABLE  project (Values, Markets, and  Policies for Biodiversity and Ecosystem Services) , funded by the  2011 ERA-Net BiodivERsA programme. DEdB and RL acknowledge the contribution of the  French Agence Nationale de la Recherche  and the  Spanish Ministry of Economics and Competitiveness , under grant numbers Convention  2011-EBID-003-01  and  PRI-PIMBDV-2011-1072 , respectively. EC also acknowledges the financial support of the  UAB-Talent Retention Programme  and notes that this work contributes to ICTA-UAB " Unit of Excellence " (MinECo,  MDM2015-0552 ).
All reports are recorded in an IATI Registry and then published on a d-portal-a country-based information platform that provides user-friendly information on resource flows. However, it does not offer any analysis or interpretation of data. Publish What You Fund, an independent not-for-profit organization tries to address this concern. They encourage visibility and quality of development aid data so that it could then be effectively used in making decisions. They do so by engaging in research and advocacy. In addition, they created the Aid Transparency Index which is "the only independent measure of aid transparency among the world's major development agencies" 2. It analyzes information provided to IATI, reports submitted by organizations on their websites and independent surveys against a certain number of criteria and then provides an annual overview and ranking of how these big donors performed.
, 2017; Pisa and Juden, 2017; Coppi and Fast, 2019). This is especially prevalent in developing countries and among the most deprived populations that the development organizations attempt to reach. In addition, some academics have raised concerns regarding ethical issues related to testing new BTs on vulnerable groups and dangers of leaking their private data to the wrong hands  (Cook, 2018; Juskalian, 2018; Zwitter and Boisse-Despiaux, 2018). There are concerns regarding security of private data of beneficiaries in digital registries, such as digital IDs. Initiatives which are collecting vast amounts of personal data of refugees to provide them with digital IDs have been frequently questioned. Also, Blockchain Technology is like any database, only a tool, which means that reliability of records would highly depend on how they were created in the first place  (Pisa and Juden, 2017).
In order to capture the precise nature of social status influence this paper therefore is one of the first studies to distinguish between ascribed and achieved social status. The only similar examination of differentiated ascribed and achieved status in a finance context was in  Lucey et al. (2012)  who analyzed acquisitiveness of CEOs of large UK companies. Compared to this study we offer a number of improvements, particularly by developing a model of status influence, applying nearest neighbor matching to identify suitable comparison firms, testing a much larger dataset with comprehensive controls, a range of acquisition methods, and investigating the market impact of acquisition announcements dependent on status. We thus arrive at a more conclusive answer as to whether social status influences CEO acquisition decision making.
We explore the influence of both status types on CEO acquisitiveness separately and further consider the combined impact of possessing high inherited as well as high attained status simultaneously. For our dataset of S&P500 CEOs we use the level of university prestige to measure CEO ascribed social status, as receiving an elite education is often argued to be indicative of an upper-class origin  (Karabel and Astin, 1975; Palmer and Barber, 2001) , Building on the work of  Malmendier and Tate (2009) ;  Cho et al. (2016)  in different areas of financial decision making, prestigious business awards are used to proxy achieved social status and we follow a nearest-neighbor matching approach adapted from  Abadie and Imbens (2011)  to isolate status influence from the selection effects. Our results show that higher social status is significantly associated with reduced M&A activity, consistent with a motivation on the part of CEOs with high social status to avoid taking risks that can harm that status.
Variables Our sample consists of yearly-rebalanced S&P 500 constituents between January 1992 and December 2012 and includes all companies except utilities, financial firms, conglomerates 2  and companies with complex governance structures (e. g. multiple simultaneous CEOs). The usable dataset is comprised of 660 companies and includes all observations for which financial and M&A information is available. The list of companies' CEOs and data on their age, tenure and gender are extracted from Compustat ExecuComp database. For the ascribed status indicator, educational background information is collected from Marquis Who's Who, Thomson One Banker, EDGAR listings, Notable Names Database, and annual reports. We use the level of university prestige to measure CEO ascribed social status and distinguish between lower and higher status CEOs based on the type of university that awarded their bachelor degree.
Therefore, our binary ascribed status measure equals to one if a CEO holds a bachelor degree from one of the Ivy League or Russell Group universities, and zero otherwise 4. For identifying achieved status shifts, award data is hand-collected from a variety of publications that conferred prestigious CEO awards during the sample period. Similar to  Malmendier and Tate (2009)  M&A data is gathered from Thomson One Banker SDC database, including transaction size, financing type, attitude, target industry. Financial controls and firm-specific characteristics are obtained from Compustat. These include firm size, Tobin's Q, return on assets, cash holdings, book leverage and industry codes for all sample companies. In addition, monthly and daily stock prices are collected from CRSP in order to estimate companies' prior performance as well as the market's reaction to M&A deal announcements.
Model 2 shows that high ascribed status CEOs invest approximately 32% less in mergers and acquisitions compared to lower status executives (p<0. 05) 7. Scaling the value of acquisitions by book value of assets rather than market capitalization makes the relationship even stronger (Model 3, p<0. 01). The coefficient for the frequency of acquisitions (Model 1) is, however, not significant. In unreported tests we find that a negative binomial model of this count variable does not improve significance, but that there is evidence of a stronger relationship in the second half of the study period (2007 onwards) compared to the first half. Models 4-6 of Table  3  confirm that the same pattern of results also holds when we expand the dataset out to include acquisitions worth more than 1% of acquirer value.
Book leverage is calculated as total debt divided by book assets. CEO age and tenure are measured in years. CEO gender is a dummy variable that equals to 1 if a CEO is a female, and equals to zero otherwise. CEO personal data is extracted from Compustat Execucomp, financial data is obtained from Compustat, performance data is collected from CRSP, and M&A data is gathered from Thomson One Banker SDC database. The column p(High -Low) shows the p-values of t-tests that the differences in means between high status CEOs and lower status CEOs are zero. Superscripts *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. tablepresentsresults of OLS regressions testing the effect of possessing high ascribed social status on CEO acquisitiveness. The dependent variables in models[1] and [4]are the number of deals worth more than 5% and 1% of acquirer's value, respectively, made in a given year.
Background: Cancer indicators are essential information for cancer surveillance and cancer research strategy development. The Martinique Cancer Registry (MCR) is a population-based cancer Registry (PBCR) that has been recording cancer data since its creation in 1981. This article provides cancer incidence and mortality data for all cancers and for major tumor sites. Methods: The registry collects all new cancer cases, details of the individual affected, tumor site and follow-up. World-standardized incidence and mortality rates were calculated, by tumor site and sex for solid tumors from the MCR database for the study period 2001-2015. Results: Over the period 2001-2015, a total of 22,801 new cases were diagnosed; 13,863 in men (60. 8%) and 8938 in women (39. 2%). In 2011-2015, 1631 new cases were diagnosed per year.
Population and design This retrospective population-based study included all incident patients with invasive cancer diagnosed in Martinique between 01/01/2001 and 12/31/2015. Data were recorded in the MCR in strict conformity with the international standards laid down by the International Agency for Research on Cancer  [5] , the French FRAN-CIM network, and the European Network of Cancer Registries (ENCR). Registry procedures were approved by the French National authority for the protection of privacy and personal data.
Statistical analysis For study purposes, we considered mortality data for all cancers and for major cancer sites: prostate, female breast, colon, rectum, stomach, and lung and bronchial cancers. The Segi-Doll world standard was used to calculate world-standardized incidence and mortality rates, by 5-year periods to enable comparisons of cancer risk between registries, independently of the effects of age  [9, 10]. Direct standardization computes the weighted average of stratum-specific estimates in the study population, using the weights from a standard or reference population. Annual rate of change (ARC) was calculated using Poisson regression and is presented as a percentage with 95% confidence interval (CI) for the 2001-2015 period and by 5-year period. Due to the incompleteness of 2012 mortality data the ARC was not estimated for mortality.
Incidence and mortality rates were plotted for individual years, except for 2012, due to the incompleteness of 2012 mortality data. We looked at all possible segments of the data by 5-year period and reported in the Results and in the Figures, those that were significant (statistically). Cumulative rates up to the age of 74 years were also presented. All analyses were performed using SAS version 9. 4. (SAS Institute Inc. , Cary, NC, USA).
Research papers have been published by the oncology department and the MCR mainly on PCa, lung cancer and colorectal cancer  [19] [20] [21] [22] [23] [24] [25] [26]. Data from Caribbean population-based cancer registries will make it possible to identify clinical and epidemiological characteristics of cancer and to study survival  [11, 23, 24, [26] [27] [28]. Leading causes of cancer mortality were analysed for the Caribbean Region during 2003-2013. Prostate cancer and lung cancer were the most frequent causes of deaths, with respectively 18. 4 to 47. 4% of cases and 5. 6 to 24. 4% of cases in men. Among women, breast (14. 0 to 29. 7%) and cervical (4. 5 to 18. 2%) cancers were the most frequent causes of deaths  [29]. Cancer is also the leading cause of death in Cuba, with 24. 9% of deaths due to cancer in 2013 (22,982 /92,273)  [30] ; lung and bronchial, prostate, breast and colon cancers were the main causes of cancer deaths.
With 44,608 new cancer cases in 2013, the highest rates of cancer incidence reported by the Cuba population-base cancer registry correspond to skin cancer, prostate, breast, lung and bronchial, cervix. The data from Cuba included non-melanoma skin cancers, which are typically excluded from most cancer data reports  [30]. In Puerto-Rico, 15,392 new cases of cancer were reported in 2012, while 5437 people died from cancer. Prostate and breast cancer were the most commonly diagnosed cancers, and the leading causes of cancer death. Colorectal cancer was the second most diagnosed cancer in both men and women. Lung cancer was the second leading cause of cancer death in men, and ranked third among causes of cancer death in women. Conversely, cancer of the colon and rectum was the third leading cause of cancer deaths among men and the second leading cause of cancer deaths in women  [31].
Acknowledgments The authors gratefully acknowledge Santé  Publique France  and  Institut National du Cancer. The authors thank all those who contributed to the recording of cancer data in the registries: the  Oncology Haematology Urology Pathology Division of the University Hospital of Martinique , the laboratories and departments of anatomy, cytology, and pathology; the departments of medical informatics of the public and private hospitals; the local offices of the national social security service; and general practitioners and specialists. We thank  Fiona Ecarnot, MSc  ( EA3920 ,  University Hospital Besancon, France ) for editorial assistance.
Empirical Illustration 4. 1 Data To illustrate how the economic and plant capacity notions can be used, we draw upon a secondary data set that is publicly available from the Journal of Applied Econometrics Data Archive. 8 This guarantees the replicability of all our empirical results. We opt for an unbalanced panel of three years  (1984) (1985) (1986)  of French fruit producers based on annual accounting data collected in a survey (see  Ivaldi, Ladoux, Ossard, and Simioni (1996)  for details). Two main criteria determined the selection of farms: (i) the production of apples must be larger than zero, and (ii) the productive acreage of the orchard must be at least five acres. Three aggregate inputs are combined to produce two outputs. The three inputs are: (i) capital (including land), (ii) labor, and (iii) materials.
Summary of the articles included in this special issue The field study by  Griffith et al. (2018)  uses the literature on substitutes for leadership  (Dionne et al. 2005 , Kerr and Jermier 1978 , Podsakoff et al. 1996)  as a point of departure to develop a conceptual model that explains work engagement and the role that managers play in facilitating it. The authors test their model using Scandinavian datasets to show that feedback from work and technological support have the strongest relationship with work engagement. They further find that supervisor electronic communication, mediated by alternative workplace use also plays a role in explaining work engagement. Overall, the findings show that complements, rather than genuine substitutes, may help managers adopt a supervisory leadership style that considers recent developments in society.
Setting Participants Students aged 18-35 years, with no known history of CVD, found at the campus during recruitment and who voluntarily agreed to be included in the study. Primary and secondary outcome measures Data were collected on personal and family history as well as lifestyle and nutritional habits; anthropometric parameters and blood pressure were also measured. Prevalence rates were calculated with their respective 95% CI. results Overall, 931 participants (53. 8% males) were included, with a median age of 23 years (IQR 21 -25). The prevalence estimates for some major CVD risk factors were: 3. 1% (95% CI 2. 0 to 4. 2) for family history of heart attack, 6. 3% (95% CI 4. 7 to 7. 9) for family history of stroke, 26. 7% (95% CI 23. 9 to 29. 5) for hazardous alcohol consumption, 0. 9% (95% 0.
Data collection Data were collected using a standardised, anonymous, pretested and partly self-administered questionnaire. It was essentially composed of two main sections: identification (sociodemographic characteristics) and medical history (family and personal medical history, and lifestyle habits). On the days of recruitment, potential participants who were found on various campuses of the University were approached by the research team. After presenting the various aspects and procedures in relation with the study, those who volunteered to be enrolled signed a consent form. Subsequently, they filled the questionnaire and underwent a brief physical examination during which anthropometric parameters (weight, height, fat mass percentage, mid-upper arm, waist and hip circumferences) and BP were measured.
Acknowledgements We are grateful the  Rectorate of the University of Yaoundé  I who authorised this study, and to all those who voluntarily participated in this study. Additionally, we thank  Dr Emmanuel Armand Kouotou ,  Dr Pierre Alain Ndzengue Ngono ,  Dr Hebert Donald Fosso Tene ,  Dr Francial Terrenstra Nang ,  Dr Dominic Leandry Angong Wouna ,  Mr Jean Jacques Baye Yomkil  and  Mr Suzel Valère Tagatsi Tala  for their help in collecting the data and/or reviewing the manuscript. Contributors JRN, FKA, JJN and JK conceived and designed the study. SJP, JRN, BSK and DNT collected the data. JRN, BSK, JJB and JJN analysed and interpreted the data. JRN wrote the first draft of the manuscript. JJB, FKA, JK, SJP, BSK, JJN, DNT and STN reviewed and revised the manuscript. All authors approved the final version of the manuscript.
In this way we connect to prior research that examines the impact of institutions on careers (e. g. ,  Cappellen & Janssens, 2010; Duberley, Cohen, & Mallon, 2006; O'Mahony & Bechky, 2006; Stovel, Savage, & Bearman, 1996). We begin with the career structuration model  (Barley, 1989; Giddens, 1984)  and explain how we use a revised version of Barley's approach by referring to the concept of mesostructures  (Maines, 1982; Maines & Charlton, 1985)  to develop a mediated model linking societal context via mesostructure to career behaviour. Next, using empirical evidence and multilevel modelling, we analyse country-level secondary data and data collected from 17,986 employees from 27 countries. Finally, we discuss the conceptual and empirical contributions of this paper and derive implications for future research and practice.
Sample and Data Collection To compile our data set, we randomly retrieved 10,750 valid email addresses of managers from the two largest professional address data providers in Switzerland and Germany. The selection criterion was that the managers were "heads" or "directors" of various departments (e. g. , marketing, research and development, production, logistics, human resources, sales). We then sent those managers an email with a link to an online survey in October 2009, with data being collected through November 2009. Identification markers prevented multiple responses from the same individual. With one reminder email, we achieved a response rate of 9. 5%. While our response rate is not ideal, studies that target managers tend to have a lower response rate than those that target non-managers  (Anseel, Lievens, Schollaert, & Choragwicka, 2010; Baruch, 1999).
In fact, a 10-12% response rate is not atypical for studies on managers from mid-sized firms  (MacDougall & Robinson, 1990; Schulze, Lubatkin, Dino, & Buchholtz, 2001; Sikora, Ferris, & Van Iddekinge, 2015). Data on all our independent variables (including perceived organizational performance) were collected in 2009 (time 1). Of the 1,024 respondents in 2009, 650 provided their email address. In May 2012, we sent them an email with a link to a second survey with identification markers (i. e. , to collect our dependent variable at time 2). This time lag of approximately 2. hard to find, we are convinced that our approach is a good compromise that balances the advantages of longer time lags (e. g. , reducing common method biases and allowing sufficient time for the observed effects to unfold) and shorter time lags (e.
Performance indicators were measured on a seven-point scale ranging from worse (1) to better (  7 ). The factor analysis extracted only one factor, with loadings of at least 0. 722. The CFI was 0. 996 and Cronbach's Alpha was 0. 87. We note that we relied on self-reported company performance data in our study. This is justified because when investigating how company performance is linked to managers' attitudes and behaviors, it is imperative to rely on individuals' perception of company performance because the same financial indicator, such as sales growth, can be interpreted differently. For example, one person might perceive 3% growth as very good, while another regards it as very poor; consequently, the effects on individuals' attitudes may be different, albeit the absolute performance indicator is the same.
In addition, we controlled for company age and size (number of full-time equivalent employees)  (Frone et al. , 1992). At the individual level, we controlled for respondent age and gender  (Demerouti et al. , 2011). To account for the family/home environment of the managers  (Anderson, Coffey, & Byerly, 2002) , we controlled for marital status (with a dummy variable coded "1" for married and "0" otherwise) and the number of children living in the managers' household (see also  Frone et al. , 1992). As we measured work-to-family conflict at time 2, one should ideally control for work-to-family conflict at time 1. Since this data was not available, we included weekly working hours as a proxy for work-to-family conflict at time 1  (Kinnunen & Mauno, 1998). Because stock ownership may affect managers' organization-related attitudes and behaviors, we used a dummy variable that indicates if the manager owned company stock  (Daily, Dalton, & Rajagopalan, 2003).