dataesr/scientific-paragraphs-categorization huggingface dataset:
New characteristics of the adoption of IT innovation since Hameed's model After examining the Science Direct and Scopus databases, as well as Google Scholar, ResearchGate, and peer-reviewed journals, we could find no research investigating Hameed's model. However, between 2012 and 2018, 41 studies were published on the adoption of IT innovation. Seven of them identified new characteristics that were not mentioned in Hameed's model, the details of which are presented in Table  1  below. Such characteristics will be taken into account in the discussion section of this article, as they can potentially provide a different perspective on our results and analysis. They could also provide potential avenues for future research. The adopted two-step flow suggests that information and influence flow from the media to opinion leaders, before being directed towards the less-interested members of the population.
Data collection and analysis To reach the research objectives, an exploratory qualitative research approach was used. In this study, we attempt to test and validate the characteristics that impact the adoption process of IT innovation featured in Hameed's model, and to identify further characteristics that have not previously been reported. We conducted 34 semi-structured interviews between October 2013 and January 2014 with 35 managers from 28 organizations in various sectors (see Appendix 1). Interviews were face-toface (20 interviews out of 34, or 59%) or conducted by telephone/Skype (14 interviews out of 34, or 41%). The objective was to meet with individuals who were interested in the subject (for example, managers who use ESN, or managers in organizations that have implemented ESN but do not use it themselves), or who were debating about whether to adopt it.
Sample Participants were recruited from two companies based, respectively, in Chile and Colombia. 1  The Chilean company has around 600 employees and operates within the media and entertainment industry. Employees of this company are located throughout Chile, with most based in Santiago, the Chilean capital. The Colombian company has approximately 300 employees and operates within the health industry, providing products for laboratories. It provides services nationally and its headquarters are in Bogota, the Colombian capital. The two companies were approached as part of a larger research project carried out by a European business school in collaboration with local business schools in Chile and Colombia. The research project, including the process of data collection, the study goals, measures, and potential publications, was explained by a member of the research team to the HR manager of each company.
As an incentive to participate, each company was offered a report and a presentation of the results from our data collection. To protect individual participants' data, these reports contained information only at an aggregate level and, as such, no individual participant's data could be discerned from the reports. We collected data from supervisor-subordinate dyads in 2015, for the Chilean sample, and in 2017, for the Columbian sample. Questionnaires for both supervisors and subordinates were back-translated from English and administered in Spanish  (Brislin, 1986). Email addresses were used to match the data for supervisor and subordinate dyads. Only the researchers had access to participant data, maintaining confidentiality of responses. Participation was entirely voluntary and participants were informed of their right to retrieve and/or withdraw their information from the study at any time.
Analytical procedure We analyzed our data using structural equation modeling (SEM). All analyses were performed with Mplus statistical software using the robust maximum-likelihood estimator (MLR), as it adjusts for skewness in the data and provides parameter estimates with robust standard errors  (Asparouhov & Muthen, 2006). Although all of our predictions were made at the individual level of analysis, our sample contains matched supervisor-subordinate data. To correct for the non-independence of subordinates' responses associated with the same supervisor, we used the TYPE = COMPLEX syntax in Mplus, which adjusts for standard errors in clustered data when parameters are estimated at the individual level. We first examined the measurement component of our model to verify the factorial structure of observed items and ensure an adequate fit with the data.
This model also failed to fit the data adequately: CFI = 0. 70; TLI = 0. 67; RMSEA = 0. 11; SRMR = 0. 25. Hypotheses H1 to H4 were examined simultaneously by estimating a structural equation model involving the following regression paths: (i) from supervisor emotional support to schedule-flexibility i-deals; (ii) from schedule-flexibility i-deals to family performance and deviant behaviors; (iii) from supervisor emotional support to family performance and deviant behaviors. The control variables were also regressed on family performance and deviant behaviors. We specified the Mplus command for estimating indirect paths from supervisors' emotional support to family performance and deviant behaviors via schedule-flexibility i-deals. This command is based on the product-of-coefficient (αβ) approach, whereby indirect effects are examined by the product of α, the regression path between the predictor and mediator, and β, the regression path between the mediator and outcome  (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002).
Geographical data and scores calculation The year of reference of the geographical data used in the classification was 2014 corresponding to the date of the last detailed French census data, provided by the National Institute for Statistics and Economic Studies (INSEE). Our study deals only with mainland France because little data were available for French overseas territories. We used the GeoFLa municipalities basemap from the National Geographic Institute (IGN) for 2016, which comprises 35,798 municipalities. For all spatial data, the projection used was the Lambert 93 being the current projection system in effect in France. As this study aims to evidence the spatial dimension of health inequalities at a fine scale, only variables measuring the contextual features and exhaustively available at the municipality scale in mainland France were selected (Table  1 ).
Environmental scores Population density The population density was calculated for 2014 using the surface area in km 2 of municipalities from the IGN's GEOFLA database, and the 2014 number of inhabitants per municipalities from the population censuses provided by INSEE. Thus, for each municipality, a population density per km 2 has been computed. Air quality Data provided by the French National Institute for Industrial Environment and Risks (INERIS) on particulate matters (PM 10 and PM 2. 5 ) and nitrogen dioxide (NO 2 ) were used to estimate the average exposure to each pollutant for each municipality between 2009 and 2013, in accordance with methods reported by Pascal et al. [26]. To elaborate the synthetic air quality score over the period 2009-2013, we considered the different spatial distribution of particulate matters in comparison of NO 2 which are more related to road traffic (Additional file 2: Fig.
S2 ). We first computed for each municipality the mean value of PM 2. 5 and PM 10 concentration in µg/m 3 as PM = mean(PM 2. 5 ;PM 10 ). Then, the air quality score was calculated, for each municipality, as the mean of PM and NO 2 as follows: Air quality score = mean(NO 2 ;PM). Risk of pesticides' exposure Due to the lack of data on pesticides applications and types used in France, we used the 2012 Corine Land Cover nomenclature to assess pesticide exposure level at the municipality scale, using the share of the municipalities' area occupied by crops using pesticides (on-irrigated arable lands, vineyards and orchards) as a proxy for exposure  [27].
Health care access scores Spatial accessibility to primary care practitioners We used the reference indices to assess the spatial accessibility of healthcare practitioners in France  [29, 30] : the APL We computed a synthetic score of the primary care accessibility at the municipality scale. Considering the importance of general practitioners in the primary care system, their APL indices count for half in the calculation of the final score, measuring the accessibility to primary care. We used principal component analysis (PCA) to summarize the other primary care practitioners' indices and selected the first component (accounting for 45% of the variance in the dataset) of the PCA as the score of these practitioners' accessibility. The final score was the mean value of both accessibility scores of general practitioners and other primary care practitioners.
Spatial accessibility to hospitals French municipalities hosting a hospital (n = 536 municipalities) were identified using the Annual Statistics of the Facilities (SAE) database (year 2014), which is managed by the French ministry of health. To measure the municipalities' remoteness to hospitals, journey times by car to the closest municipality hosting a hospital were estimated using the Odomatrix ® software (Source: ODOMATRIX, INRA UMR1041 CESAER, from IGN Route500 ® ).
Preliminary analysis of the dataset and clustering process of the French municipalities A correlation matrix was performed using the R software (version 3. 5, package "Corrplot") to study potential correlation between the selected variables and to justify the relevance of each variable to be included in the clustering process. Several methodologies are used to develop multivariate classification of areas  [31]. Hierarchical clustering and partitioning methods have their limitations but hierarchical clustering is known to be more computationally demanding and less efficient than partitioning methods with large datasets  [32]. Considering our large dataset (ten variables, 35,798 municipalities), we selected the K-means partitioning algorithm using the R software (version 3. 5, packages "NbClust") to perform the classification.
Geographical variables were standardized using the R's "scale" function, before incorporating them into the clustering process. We performed several K-means partitioning from the same dataset in order to ensure the clustering process' stability and the obtained classification was always the same. While the classification aims to allow health outcomes' comparison across geographical contexts, we were careful not to deal with too heterogeneous clusters considering their population size in order to be statistically comparable. We chose therefore the five-cluster partitioning because the six-cluster partitioning splits the RM (Rural Margins) cluster in two parts whereas the population size of this cluster was already the lowest.
Calculation of the age-standardized mortality rates according to the GeoClasH clusters In order to assess the relevance of the classification to display health inequalities, the age-standardized mortality ratio (SMR) and their 95% confidence interval (CIs) was calculated in each cluster, as the ratio of observed deaths and the expected deaths in each cluster based on the distribution of mainland France, using the indirect method. The expected deaths were based on agespecific deaths in the mainland France. We obtained data on overall population and mortality from the INSEE databases, for the mainland France at the municipality scale, in 5-year age groups (i. e. 0-4, 5-9… 80-84, and ≥ 85 years) from 2011 to 2015 [33]. Data were not available at the municipality scale to standardize also on sex distribution.
The classification can be used for recent years but can also be valid for last decades due to small changes and constant trends in the French geographical area regarding variables included in the classification. Our methodology can be potentially reproducible in other countries and scalable with different number of clusters, in accordance with the dataset. As an example, air pollution data used in this study can be obtained in other European countries as well as the data used to estimate the probability of pesticide use  [42]. Our results report significant health inequalities according to the GeoClasH clusters and especially the positive situation of Wealthy Metropolitan Areas (WMA) in comparison to mainland France, with a 15. 1% lower mortality. These results are particularly remarkable because the GeoClasH classification was only designed on theoretical basis to demonstrate the complex constitution of spatial inequalities in health which result from the combining effects of local health care accessibility, environmental and social characteristics.
We have therefore opted for the municipal level to protect the balance of the Geo-ClasH classification and promote its compatibility and its implementation with most health studies. Among the variables considered, only data with good temporal consistency and with a sufficiently precise scale has been used. Therefore, water quality data have not been integrated because of the low number of sampling in sparsely populated areas. For example, weekly biological measurements were made in some densely populated areas while just one value in 5 years was available for sparsely populated areas. The low number of samplings raises question about the representativeness of these few measurements and could also introduce a potential bias between urban and countryside municipalities. We only had annual data on PM and NO 2 concentrations and it would be interesting to use hourly data to consider the number of threshold exceedances that are known to have short term effects on health  [43].
Corine Land Cover data used to estimate environmental exposures to pesticide were not initially designed to this purpose even if several studies recognized Corine Land Cover data as the most suitable database available for national scale studies  [27, 44]. Crop density by municipality has been used in ecological studies as a surrogate for residential exposure to agricultural pesticides  [45]. Each plot (25 ha) has been characterized by its most extensive crop that may lead to not considering small specific acreage within the plot. Data to provide a nationwide measure of food, alcohol and tobacco retail environments are not currently available in France  [46]. Similarly, we do not currently have consistent, reliable and comprehensive data in France to measure spatial coverage dedicated to greenspaces  [12, 13].
Acknowledgements We would like to thank  Laure Malherbe  and  Laurent Letinois  ( INERIS ) for its expertise on the  CYSCLAD  project and the data processing,  Veronique Lucas-Gabrielli  and  Charlène Le Neindre (IRDES)  for their advice on the measure of health care accessibility in France,  Thomas Coudon  and  Lény Grassot  ( Centre Léon Bérard ) for their advice on air quality measure and statistical analysis,  Conor Macdonald  ( Center of Epidemiology and Population Health ) for the English review, as well as  Marc Billaud  ( Centre Léon Bérard ) for his support.
ACKNOWLEDGEMENTS We are grateful to  Joshua Wietz  for critical review of the manuscript and valuable comment. We are similarly grateful to  Silvia De Monte ,  Guilhem Doulcier ,  Médéric Diard  and members of our respective teams for lively discussion. AJB acknowledges an  ARC   DECRA fellowship  ( DE160100690 ) and support from both the  ARC Centre of Excellence for Mathematical and Statistical Frontiers (CoE ACEMS) , and the  Australian Government NHMRC Centre for Research Excellence in Policy Relevant Infectious diseases Simulation and Mathematical Modelling  ( CRE PRISM 2  ). PB acknowledges a  Macquarie University Research Fellowship  and a Large Grant from the  John Templeton Foundation  (Grant  ID 60811 ). PBR acknowledges generous financial support from  MPG  core funding and previously from the  Marsden Fund Council from New Zealand Government  funding, administered by the  Royal Society of New Zealand.
Study setting French Guiana's sole correctional facility includes a prison for males (« centre de détention », for persons sentenced for more than 2 years), a jail for males and a jail for women (« maison d'arrêt », for persons waiting for trial or sentenced for a duration shorter than 2 years)  [6]. HIV testing and ARV treatments are proposed to any incarcerated patients, according to the French National recommendations  [10]. Before the last recommendation issued at the end of 2013, the threshold to start ART was 500 CD4 cells/mm3  [11]. After HIV positive confirmation, all HIV-positive patients were registered in the electronic medical record NADIS, used at the regional level for HIV-follow-up, and gave written consent for electronic data entry, data analysis, and research publication. NADIS on-line software was shared and available for any HIV specialist working in French Guiana.
The DAI-VIH retrospective cohort The DAI-VIH retrospective cohort was conducted among all the HIV-infected adults (> 18 years old) released between 1/1/2007 and 12/31/2013, after an incarceration of 1 month or more. The last incarceration of the time period was defined as incarceration of reference  [6]. The main source of data was the electronic medical records NADIS. Additional data were collected from the correctional facility paper files, the registries of the three regional hospitals, and the municipal death registries of the main towns of French Guiana (covering 80% of the French Guianese population). Identification relied on name and date of birth and was performed by a medical doctor. Data were recorded manually and anonymized, by the same person. The data collected were socio-demographic characteristics, number of past incarcerations, date of entry and release of the index incarceration, medical and psychiatric background, substance abuse, HIV history, comorbidity, HIV-clinical stage, opportunistic infections, immunological and virological results during the index incarceration, post-release events: consultation in HIV care units, unscheduled emergency consultations, hospitalizations, occurrences of opportunistic infections or any CDC-stage C events, further incarcerations, date and cause of death.
We estimate these concentrations using the COPERT IV model  (Ntziachristos et al. , 2009). Note that, COPERT IV is an aggregated model, i. e. applicable to a region or zone of a city network, that takes as an input an average speed and total travel distance. For each mean speed value, the model already includes the driving cycles that account for accelerations and decelerations of vehicles. The calculation of the Emission Factors of CO 2 (EF CO 2 ) and NO x (EF NO x ) are based on reference emission data recorded for a mean speed profile of private cars. We further assume a homogeneous fleet over the whole network. Figure  6  depicts the emission laws for the Emission Factors of CO 2 and NO x. Figure  7  depicts the evolution of the vehicles' mean speeds v r (n r ) for the DUE, SUE and the three settings of ∆ OD for the bounded rational users with indifferent preferences.
Acknowledgements The authors wish to thank all the members of the communities in which this work was conducted. The authors would like to express their gratitude to the referees for their insightful comments. They also wish to thank  John Baker  for his careful revision of the language. Funding: the analysis conducted, and drafting of this paper was made in the course of the  CoCliServ  project. Project  CoCliServ  is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate , and funded by  FORMAS (SE) ,  BELSPO (BE) ,  BMBF (DE) ,  BMWFW (AT) ,  IFD (DK) ,  MINECO  ( ES ),  ANR (FR)  with co-funding by the  European Union  (Grant  690462 ). The data collection process, and the associated project, received financial support from the  Belmont Forum International Opportunities Fund , with contributions from the  Agence Nationale de la Recherche, France , the  National Science Foundation, USA , the  Ministry of Earth Science ,.
White-collar crime: owners' and executives' crime against their employees and/or clients Corruption for the allocation of mega-sporting event  (Chappelet & Kübler-Mabbott, 2008)  or in betting scandals with government's ministers  (Lee, 2008)  (Those examples are not directly linked to the economics of crime) Occupational crime: employees' crime against their organisation (theft and embezzlement) Match fixing involving players  (Caruso, 2008; Hill, 2009)  Economics of doping  (Berentsen, 2002; Maennig, 2002)  The crime economy examines street and domestic crime using an empirical approach. Econometrics therefore allows academics to analyse large databases to see the effect a match has on criminal activity. However, a theoretical approach is more often employed when analysing occupational crime.
Research Design We build a cross-sectional (not cross-temporal) dataset including 155 observations (one by country). Indicators pertain to the intensity of each pattern of ISIS violence for each country as well as to national indicators of socio-economic conditions and military interventionism. The main reason for using a simple cross-sectional design is the lack of reliable longitudinal data regarding key variables in the analysis 6. Note that Study 2 specifically addresses the limitations of this simple design by using longitudinal data. We first operationalize our dependent variables by estimating the incidence of the three patterns of ISIS violence in a given country. Regarding foreign fighting in Iraq and Syria we rely on data from the Soufan Group assembled by  Benmelech and Klor (2018) , which estimate a count of foreign fighters joining ISIS from all countries.
The Soufan Group claimed to rely on official government estimates wherever possible but also from other reliable sources (Soufan  Group 2015). The data inevitably suffer from limitations such as estimates biases but also the lack of government official counts for some countries. However, to our knowledge, this source provides the most systematical and accurate estimates of foreign fighters flows, and is the reference database for cross-national comparisons of foreign fighters flows  (Benmelech and Klor 2018, Gouda and Marktanner 2019). For terrorist attacks related to ISIS, we use the Global Terrorism Database (GTD) (START 2017). The GTD is the most extensive event database on terrorist attacks worldwide (LaFree, Dugan, and Miller 2014) and allows identifying the organization responsible for the attack.
In the case of ISIS, an auxiliary dataset has been recently released to further disentangle attacks related to the group  (Miller et al. 2016). This dataset, updated until December 2016, enables Table  A1  in appendix lists the date and location of the 44 lone wolves attacks identified for our study period 9. Our own research based on public sources shows that three quarters are generally classified as domestic (based on an identity relationship between the venue, target, and perpetrators). Since H2 expects cross-national variation in the number of lone wolves attacks to be positively associated to both military interventionism against ISIS and to grievances and opportunity costs, we excludefor methodological reasons -cases involving foreign perpetrators. By excluding minority transnational attacks, we restrict the analyses to attacks considered as domestic to determine to what extent international target selection causes influence them.
Affiliated terrorists attacks implies that the leadership of ISIS took an active part in the planning of the attack. Table  A2  in appendix lists the date and location of the 113 attacks identified for our study period. In two thirds of the cases, attacks cannot easily be categorized as the identity of the perpetrator is unknownmost of them involve events resulting from a spill-over of the Syrian civil war to Lebanon and Turkey. 12% of the remaining cases are perpetrated by national citizens and can be defined as domestic. The other cases either involve dataset in our analyses. However, we conducted research based on public sources for each attack (see tables A1 and A2 in the appendix) and identified attacks where the link with ISIS is contested. The conclusions of the paper are unchanged when excluding these cases.