dataesr/scientific-paragraphs-categorization huggingface dataset:
RELATED WORK The use of MDE for development and automatic generation of web services or web applications is not a new topic  (Bernardi, Cimitile, Di Lucca, et al. 2012; Bernardi, Cimitile, and Maggi 2016; Rocheteau and Sferruzza 2016; Scheidgen, Efftinge, and Marticke 2016). This work is built on the approach of REIFIER, presented in  (Rocheteau and Sferruzza 2016). As in REIFIER, we use a meta-model approach instead of a meta-programming one, as recommended for serverside code generation in  (Scheidgen, Efftinge, and Marticke 2016). We also share the meta-modeling approach with the M3D tool introduced in  (Bernardi, Cimitile, Di Lucca, et al. 2012 ) and extended in  (Bernardi, Cimitile, and Maggi 2016) , which is itself based on Declare (see (van der Aalst, Pesic, and Schonenberg 2009)). M3D uses as an input a 4-layers meta-model: information layer by means of UML class diagram, service layer by means of BPMN, presentation layer by means of (Spring/Android)-like meta-model and a process layer by means of Declare.
Something entrepreneurs should know on insolvency Whatever its nature, bankruptcy is accompanied by set of foreseeable and unexpected consequences that entrepreneurs and policy makers should know to contribute to the organization of a performing entrepreneurial system. The papers selected for the special issue have in common the attention paid to the influence of the institutional and economic framework on bankruptcy and on the reciprocal relationship since it is clearly established that the risk of bankruptcy shapes the economy. They provide empirical evidence on the diversity of the transmission channels thanks to the use of various individual, local, and national datasets covering different countries and periods. A broad and extensive analysis of the interplay between insolvency regulation and entrepreneurship is provided by Kun Fu, Karl Wennberg, and Björn Falkenhall (this issue) in a paper showing that if firm creation is influenced by corporate bankruptcy laws, this relationship is different according to the type of rules and the type of entrepreneurship considered.
The richness of the dataset used and the originality of the estimation technique implemented combine to provide robust and original results. Indeed, using multilevel modeling of approximately 300,000 individuals in 27 countries over the 2005-2010 period enables the authors to disentangle the somehow simplistic direct relationship between the legal context and the propensity of individuals to start a business, with major attention being paid to the nature of the entrepreneurial process. The distinction between opportunity-driven, necessity-driven, and innovative entrepreneurship contributes to a better understanding of the relationship between bankruptcy laws and entrepreneurship. When reforming the rules, policy makers should thus pay attention to the type of entrepreneurship affected by the changes introduced.
The results obtained show that fraudulent behavior should not be forgotten, that fraudulent bankruptcies depend on the legal context, and that destructive entrepreneurship may have effects on the economic system. This indirect and often hidden effect of bankruptcy regimes should merit the attention of legislators whenever they propose a change in the law, mainly because nothing can change if the agents primarily targeted by the reform maintain their deviant behavior. As clearly stressed formerly, however, cheating does not motivate all entrepreneurs and bankruptcy is rarely the outcome of criminal intent. Rather, a large share of entrepreneurs fight hard to support their activity during crisis periods, as discussed by the paper of Marco Cucculelli and Valentina Peruzzi (this issue). Thanks to empirical analysis of a dataset covering 67,241 Italian manufacturing firms, this research clarifies the relationship between post-crisis firm survival, business model changes, and organizational learning, an explanatory chain that puts some emphasis on business model changes as a critical condition of survival.
Employees are also concerned, as shown by Kristina Nyström in a paper (this issue) that raises the question of the capacity and willingness of displaced employees to create their own business. Whereas many efforts are devoted to promoting firm creation by employees fired after a firm closure, this phenomenon is still barely documented, mainly because of the lack of available data. Using a unique dataset of matched employer-employees providing information on entrepreneurial ventures started by displaced employees in Sweden during the period of 2001-2010, this research makes a great contribution to the understanding of dismissed employees' motivation and shows that all of them are far from equal in entrepreneurship and that the businesses they launch are fragile. This result should lead to a rethinking of policies promoting entrepreneurship as a solution to unemployment.
Instances C++11 has been chosen for the implementations and Gurobi 6. 5. 1 was used to solve the model on a PC with Intel(R) Core(TM) i7-3770 CPU @ 3. 40GHz and 8Go RAM. The instances used are realistic and have been provided by a client of the partner. The client gave actually the figures of seven assembly lines but we give the results for only two of them: L2 and L6. The line L2 experiences overcapacity: the ratio expected demand over total capacity is smaller than 1 (actually equal to 0. 7). The line L6 experiences undercapacity: the ratio expected demand over total capacity is larger than 1 (actually equal to 1. 1). The horizon T is the typical one used in practice by this client, namely T = 13 weeks (a quarter). The demand is obtained via d r t = ( dr t + e r t ) + , where the dr t 's are historical data and where e r t is a generalized autoregressive process so that e r t = 0.
The 2SA-m algorithm requires 90 seconds to output a solution, while lot-size and cover-size heuristics take less than a second and the deterministic approximation less than 10 seconds. The 2SA-m algorithm is thus slower, but note that 90 seconds to be run only once at the beginning of each week remains very short. Moreover, even with improved computers, the lot-size and cover-size heuristics and the deterministic approximation will not change their output (in all our experiments, Gurobi always found the optimal solution of the deterministic approximation). This is not the case for 2SA-m, which means that it would benefit from improved computational capacities. Otherwise, complementary figures not provided here show that the deterministic approximation and 2SA-m use almost every available setups (between 97% and 100%) whereas lot-size and cover-size heuristics cannot take full advantage of this flexibility (only 84% to 98% of setups are used).
These included participating in the executive board meetings governing the relationship, a series of informal interactions with cluster members, and the semi structure interviews described above. The participant observations were collected from the regular attendance of the Executive Board of the relationship, these occurred monthly at first and then quarterly between the second and third years of data collection. Additionally, data was collected from a separate form of direct observations, in which the author had adopted a passive role (e. g. monthly technical meetings). These direct observations allowed the researcher to retain a more passive role while observing interaction patterns and activities in a natural setting. This allowed for observations of the interaction patterns among specific actors without interfering in the overall design of the event  (Silverman, 2015; Yin, 2013).
Due to the sensitive and strategic nature of the content and to ensure confidentiality of the content being shared, the direct observations were not digitally recorded but field notes were permitted. The direct observations were collected through the attendance of (1) monthly technical meetings and (2) presentations that were focused on the relational context for public audiences. The meetings provided the relationship with a forum, where projects were presented to partners for knowledge progression and joint problem solving. Field observations continued throughout the length of the project. The repeated observations and an intensive long-term involvement supported the development of an indepth understanding of the phenomenon  (Maxwell, 2009). This technique provided the opportunity to collect rich data through gaining a focus on the natural and real-life settings  (Miles and Huberman, 1994).
It is frequently applied within anthropological studies with the intention to understand naturally occurring phenomena and ordinary events in natural settings to develop a strong understanding of what real-life is like therein  (Silverman, 2015). Finally, the author had extensive access to relational development and coordination documents. These documents were initially intended for the management and coordination of the relationship and have allowed for a further longitudinal lens into the transactional/formal coordination of the relationship and the organizing techniques dating. Due to the congenial access that the author was provided, the author had access to the relational management historical database from the point of initiation in 2011. This secondary data enabled further triangulation and analysis for the purposes of the questions at hand  (Glass, 1976).
Secondary data were collected from internal documents, presentation materials from workshops and conferences, as well as information displayed in the public domain, such as press releases, books, articles, and website information. The benefit of retrieving the archival documents was that they augment evidence from other sources to identify corroboratory versus contradictory evidence  (Wasserman and Faust, 1994). The key benefit of this secondary data access was to further explore the involvement and intentions of the network members, as well as extracting further information regarding the relations that exist in an objective way.
Analysis Methods Utilising a process of interpretive categorization, the analysis began with a data reduction process in which a comprehensive database was drafted and all data labelled according to emerging first-order themes  (Miles and Huberman, 1994; Gioia et al. , 2013). All documents, field notes, and interviews were stored in electronic format and compiled into an electronic database. To allow for new patterns to be explored and emerge in an inductive way  (Yin, 1994) , data was assessed by way of thematic analysis  (Miles and Huberman, 1994; Creswell, 2009). This enabled a more comprehensive opportunity to make sense of the ways in which the relational activities and their mechanisms emerged and formed. In the process of compilation, data was interpreted and tabularized according to the emerging first-order thematic codes.
Throughout the data collection, an iterative process of identifying themes continued until theoretical saturation and fresh insights could no longer be yielded  (Yin, 1994). As the analysis has focused on refining the structure of networked exchange and distilling compositional features, the early phases of the work also focused on providing a continual comparison of the actor-level features and their interactions amongst entities and sub-units  (Wasserman and Faust, 1994; Gephart, 2004). This allowed the researchers to refine the theoretical similarities and differences amongst actors and more accurately engage with categorization. In allowing for fairness and range, data was then assessed into a chronology of core events and exhibited behaviours to allow for an overview of evolutionary processes.
Tied to this, the success of the activities of one group can filter through to another group due to in-group membership and the representations of the self. Within in-group membership, achievements are identified among the group members as a whole. Ingratiation (conveying the impression of being likable), self-promotion, and competence display are common goals of social interaction  (Arkin, 1981; Learny, 1995). While the research design used multiple data points to allow for triangulation, which further allowed for the verification of the existing linkages, some of the network externality data was collected via self-reporting mechanisms. Future research studies may wish to focus on these individual-and group-level matters to further examine the functioning of relationships within a bounded network cluster from and through a holistic observation of both focal and peripheral network actors.
Business units Some PMI studies focus on business units as the subjects of integration. For instance,  Barkema and Schijven (2008)  suggest that integration decisions at the business unit level are initially suboptimal and that businesses are only holistically integrated at a later point. A challenge when looking at the reconfiguration of business units is the availability of data because evidence of how business units within a firm are modified over time is rarely observable. Barkema and Schijven (2008)  collected data from annual reports of large Dutch firms. Karim (2006)  chose a context that would allow insight into business units-the US medical sector. Observing the business units through detailed analysis of reports, she also tackled the challenge of finding a counterfactual, comparing the modifications of acquired business units to those of internally developed business units.
Outcomes of PMI The consequences of integration choices are often the focus of PMI studies, notably in two respects-in financial terms and in terms of innovation. Financial outcomes can be measured either as return on assets (ROA), considering changes before and after an acquisition  (Barkema and Schijven 2008; Zollo and Singh 2004) , as stock returns for public acquirers through event studies  (Capron and Pistre 2002) , or changes in productivity  (Bertrand and Capron 2015). When data is unavailable or hard to obtain-which is often the case for data on targets-estimations of financial performance can be assessed through surveys  (Capron 1999). Innovation outcomes can be measured as the count of new products, which may be available from annual reports  (Puranam et al. 2006) , or as the number of patents secured  (Ahuja and Katila 2001; Kapoor and Lim 2007).
Setting and operationalization Once a research question has been defined, it is necessary to identify an empirical setting in which it can be tested. Researchers tend to choose settings in which M&A is a fairly frequent strategic action of firms. For instance,  Zollo and Singh (2004)  chose the US banking industry in a phase of consolidation precisely because of the increased number of observations. In these various settings, researchers have used different methods to gather private data on integration, often complemented with data from publicly available sources. Because of its complexity and the often fine-grained nature of its data, PMI research can benefit from qualitative inductive work. For instance,  Bresman et al. (1999)  used the special insights that qualitative work offers investigate the transfer of knowledge in PMI processes.
With its complex object of analysis, PMI research can also deepen the understanding of distinct aspects of the process by resorting to experiments. For instance,  Weber and Camerer (2003)  performed a laboratory experiment where they grouped their participants in order to simulate cooperation in firms. In a second step, participants of two separate groups were told to work together as a merged unit. The new unit being less efficient, members maintained an overestimated perception of their performance and attributed decreases to other units. In this type of setting, researchers could simulate similar key aspects of PMI processes. The use of surveys can also be an effective way of accessing fine-grained data on a larger scale, such as those on resource redeployment, the mobility of people, and the nature of tensions in the PMI process  (Capron 1999; Cording et al.
2008; Bauer and Matzler 2014). For instance,  Capron (1999)  used a survey to investigate the extent of resources being redeployed between acquirer and target. Zollo and Singh (2004)  used a survey-based approach to explore the link between codified and tacit knowledge from experience and financial performance in the US banking industry. Questionnaires can also be used in combination with other coded or hand-collected data. Zollo and Singh (2004)  assessed the extent of PMI through a survey and measured knowledge codification, an additional explanatory variable, as the count of manuals and guidelines within the firm. Researchers have also found settings in which they can observe and hand collect data on PMI (e. g. ,  Karim and Mitchell 2000; Krishnan et al. 2004). Karim (2006) , for example, found a suitable setting in the medical sector where reports gave information about which product lines each firm pursues.
Barkema and Schijven (2008)  coded restructuring efforts from the annual reports of Dutch multinationals to study the unlocking of synergies through PMI. As for secondary databases, we outline some data sources below that enable researchers to collect information on deal characteristics, acquirer-target relations, outcomes, and integration. Electronic databases contain useful information on deals, acquirers, and targets. The deals themselves can be identified in SDC Platinum, and information on the involved firms is often available in COMPUSTAT. In these databases, researchers have the opportunity to select companies belonging only to certain industries based on SIC codes. PMI research has in the past focused on the pharmaceutical industry  (Paruchuri et al. 2006; Schweizer 2005) , communication and information technology  (Graebner 2004; Puranam et al.
2006) , the health care industry  (Karim 2006) , the manufacturing industry  (Capron 1999) , and the banking industry  (Zollo and Singh 2004). Holding the industry constant may allow a sample to be identified faces relatively similar environmental conditions. Technology firms are often used as the setting when interested in knowledge outputs and exchange in M&A as they produce patents. Data on innovation outcomes can stem from multiple sources. For instance, product launches can be observed through press releases which can be found in databases such as Lexis-Nexis (e. g. ,  Paruchuri et al. 2006). Patent data has also been used to look at common patent activity between targets and acquirers, available through the US Patent and Trademark Office (USPTO). For technology acquisitions, partial information on integration is accessible through CORPTECH, which holds profiles and descriptions of technology firms.
Both these dependent variables, the duration until PMI or outcomes of PMI, can be specified as explained by time and a set of covariates and parameters. We distinguish between continuous and discrete time models. In the case of PMI outcomes or choices, the events of interest can most likely occur at any point in a time continuum. While PMI data will usually be restricted to a more discrete form, whereby time is grouped into specific intervals, the choice between discrete and continuous time models can be guided by the underlying phenomenon. Thus, although PMI choices and outcomes might be observed in discrete intervals, PMI research often makes use of continuous time models that do not rely on specific assumptions about underlying hazard functions. For instance,  Puranam et al. (2006)  use a proportional hazard rate model to assess the hazard of a new product introduction, where integration is one of the covariates of interest.
Acknowledgments The authors are grateful to the M. Sc. candidates who contributed in the collection and analysis of data:  Sarah-Jane Parker  from the  University of Stirling ,  Gonzalo 'Jun' Coloma  from  BFAR, Anne-Laure Chaigne  from  Montpellier SupAgro ,  Anne-Charlotte Talbot  from the  University of Clermont-Ferrand ,  Pascal Levy  from  ISTOM ,  Damien Ollivry  and  Bertha Iris Argueta  from  Science-Po Paris. They are also grateful to the staff of the  Sasmuan Municipal Office  and the staff of the  BFAR Central & Region 3 Offices  for their continual support and help in the collection of the data and in particular,  Marita Ocampo ,  Amor Diaz ,  Jun Coloma ,  Lilia Pelayo ,  Simeona Regidor ,  Sonia Somga  and  Malcolm I. Sarmiento  (Director of BFAR during the investigations). Finally, the authors are grateful to Cirad that funded this research.
The study analyses the economic consequences of changes in the local bank presence. Using a unique dataset of banks, firms, and counties in Poland over the period 2009-2014, we show that changes in local banking that increase the role of the relationship banking model are associated with improvements in local labour markets and easier access of SMEs to bank debt. Moreover, radical changes in the ownership structure of large commercial banks result in a more rapid new firm creation. Finally, we document that young companies' performance is more sensitive to the instability of local banking markets.
Our study tries to fill in this gap in the literature. To examine the local repercussions of crisis-induced modifications in the banking sector, we use data from Poland from 2009 to 2014. We focus on three types of changes in the local bank presence. First, we study the dynamics of bank branch networks within local banking markets. Second, we investigate the changes in the number of banks. Finally, we analyse the modifications of bank ownership structures within local banking markets. We investigate local changes at the county level 1. We assess the effects of instability in local banking markets from the perspective of local economic activity and firms' performance. Consequently, our dataset combines information on a county's economic situation, local firms' financial characteristics, and bank branch locations.
Young companies demonstrated to be more sensitive to the instability of local banking markets than mature firms. For example, the negative influence of large commercial banks' entries on the long-term debt growth ratios was stronger for SMEs that were established in the previous five years. However, the same group of SMEs benefited the most from the increased number of cooperative banks within a county in terms of reduced financial costs. Our study contributes to the existing literature in three aspects. First, we use a unique dataset that allows us to trace different types of changes in the local bank presence and to assess their influence on local economic activity in the post-crisis period. Second, our study adds to the relatively small number of studies that address SMEs' performance drivers in emerging economies, in general, and in Central European (CE) countries in particular.
Local Firms' Outcomes To examine the impact of local banking market instability on already established SMEs, we apply the GMM-SYS procedure to dynamic panel models using a dataset composed of firmyear observations. Equation (  2 ) illustrates the general construction of the models: years and industries. 7 To test our research hypothesis regarding the impact of the local banking market instability on young companies (not more than 5 years old), we introduce binary variables identifying young companies (YOUNGitk) and their interaction terms with different market instability measures (YOUNGitk x INSTBitk-1). Panel C in Table  1  presents the detailed definitions of all firm-level variables, while Panel C in Table  2  reports the corresponding descriptive statistics.           dummies.