dataesr/scientific-paragraphs-categorization huggingface dataset:
 Emotional intensity; Nodes are asked whether they feel friendly, affectionate or emotional closeness to the named actor or whether he or she is merely an acquaintance.  Intimacy (mutual confiding); Nodes are asked whether they share intimate spaces or activities with the named actor.  Mutual services or Resources; Nodes are asked whether favours (of any type) have been given or received between both actors in the dyadic relationship. Mutual or non-mutual favours, but which have occurred. 3) Network structure analysis: using measurement of density, nodal degree and intermediation (betweeness) to determine SNA central actors using the UCINET 6 computer program  (Borgatti et al. , 2002).
Existence of bridging social capital in El Priorat To verify the presence of bridging social capital among El Priorat's wineproducers, we used the information gathered during the interviews and some data from the Regulating Council of the DOQ Priorat in order to reconstruct the historical panorama of before and after the arrival of innovative entrepreneurs alien to El Priorat (4 pillars). The first significant data are the rising figures for export activity that currently exemplify the wine industry in El Priorat (see Figure  3 ). ---Insert Figure  3  about here ---This boom in DOQ Priorat wine exports in the 1990s, initiated by the bridging agents locally called the 4 pillars, implicitly meant an increase in the interactions between local entrepreneurs and external agents, such as distributors, entrepreneurs in the catering sector, sommeliers, specialised journalists, possible partners, etc.
Data sources and main variables The data on ownership come from the Bureau van Dijk databases. 7 For the period of 2006-2009, we use Orbis database, while for the period 1999-2005, we combine Amadeus and Osiris databases. Orbis contains ownership, financial, and corporate governance data for over 16 million firms across the globe (as of July 2008). Amadeus is a subset of Orbis for both listed and nonlisted European firms, while Osiris is a subset of Orbis for all global listed firms. The data on ownership by institutional investors ("institutions") come from FactSet/LionShares. FactSet/LionShares compiles institutional ownership from public filings by investors (such as 13-F filings in the U. S. ), company annual reports, stock exchanges, and regulatory agencies around the world. Institutions are defined as professional money managers, including mutual fund companies, pension funds, bank trusts, and insurance companies.
We consider all types of stock holdings: common shares, preferred shares, American Depositary Receipts (ADRs), Global Depositary Receipts (GDRs), and dual listings. FactSet/LionShares provides holdings data by over 5300 institutions, with positions totaling US$17 trillion as of December 2007. Ferreira and Matos (2008)  provide a more detailed description of this database. A majority (63. 33%) of institutional investors report stock ownership semiannually, 30. 04% report on the quarterly basis, 6. 08% do it yearly and 0. 55% report monthly. Hence, we execute our cross-sectional analysis on the semiannual basis. We focus on publicly 7 Bureau van Dijk describes its collection of ownership data as follows: "For US listed companies, ownership information is systematically collected from the Free Edgar File which includes all companies filing proxy statements.
These links cover all known shareholders (corporations or individuals) with an ownership percentage of 5% or more, as well as the ownership of directors and executive officers (with no lower ownership percentage limitation). Data is gathered tracking lower levels percentages owned by corporations. This is done by querying the NASDAQ web-site under the entry "Beneficial Owner" which is associated to the display of a company. (This covers all companies listed in the US stock exchanges, not only those listed on the NASDAQ). " For non-US firms and US private firms Bureau van Dijk collects data from annual reports, stock exchanges, information providers, company web-sites, press news, and private correspondence (with a 25% response rate). This implies that the data are collected in a similar manner as in other related studies.
We confirm this by comparing summary statistics. Our summary statistics are comparable to those in  Dlugosz et al. (2006) ,  Villalonga and Amit (2006)  and other studies on blockownership in US public firms. We attribute the fact that the mean blockholding in our sample is slightly higher than in other studies to our larger sample and thus higher proportion of small firms. listed corporations worldwide for which we are able to obtain financial data and accounting data, stock market information from Datastream/WorldScope and country level measures of governance. We exclude US based companies from our analysis. The final sample consists of 10,923 firms in 29 countries. The frequency distribution of firms across the countries is presented in Table  1 , Panel A. Larger economies lead the way with the number of firms represented; there are 3139 Japanese firms and 1027 UK firms.
At the same time, even for countries with the smallest number of firms in the sample (Portugal with 33, Argentina with 42, and Ireland with 44) there is a large enough sample to execute within-country analysis. Data on accounting variables come from Datastream/Worldscope. Appendix provides a description of both the data sources and the main variables. We match Bureau van Dijk data and Datastream/Worldscope with data on institutional investor stock holdings from FactSet/ LionShares over 1999-2009. The descriptive statistics of the main variables are in Table  1 , Panel B. The firms in our sample have a median level of profitability (ROE), cash, tangibility and dividend yield (scaled up by 100) respectively equal to 0. 083, 0. 153, 0. 257, and 1. 953. The descriptive statistics of our variables matches very closely that of  Ferreira and Matos (2008)  who use the same data, but over a shorter time period.
We use two measures of "horizontal governance": a "revised anti-directorship index" (horizontal, H1) of La  Porta et al. (1997) , and an "anti-self-dealing index" (H2) of Djankov et al. (2008). 8 These are intended to measure the degree of legal protection of minority shareholders against expropriation by corporate insiders. We use three measures of "vertical governance": "constraint on executive power" (vertical, V1) from the Polity IV dataset  (Gurr, 1997) ; "protection against expropriation by government" (V2) from Political Risk Services  (Knack and Keefer, 1995) ; and "quality of institutions" (V3) from the International Country Risk Guide  (Bekaert, et al. , 2011). We provide a detailed description of all governance measures in the Appendix. The correlations between measures of vertical and horizontal governance are low: on average they are 19.
Originally constructed and described in La  Porta et al. (1997). Revised in  Djankov et al. (2008). Anti-Self-Dealing Index an index of the strength of minority shareholder protection against self-dealing by the controlling shareholder; constructed and described in  Djankov et al (2008). Constraint on Executive Power seven-category scale, from 1 to 7, where a higher score indicates more constraint: 1 indicates unlimited authority; 3, slight to moderate limitations; 5, substantial limitations; 7, executive parity or subordination; 2, 4, and 6, intermediate values; obtained from Polity IV dataset. Variable described in  Gurr (1997). Protection Against Expropriation by Government risk of expropriation of private foreign investment, from 0 to 10, where a higher score means less risk; obtained from Political Risk Services; variable described by  Knack and Keefer (1995).
This magazine contains articles on issues relevant to investment managers, together with adverts produced by market venues, data vendors, investment banks and brokerage companies. In 2007, The Trade began organizing TradeTech, an annual fair where brokerage companies, data vendors, investment managers and regulators could gather and share ideas. MiFID gave its impetus to the event, as it required substantial technological changes and investments from market participants. We have examined forty-nine issues of The Trade published in the period 2006-2016 and the satellite publication The TradeTech Daily, which is the official daily newspaper published each year during the TradeTech event. In so doing, we built a database of all the adverts related to dark-pool services. The first element worth noting is the attempt of all dark-pool providers to sell their execution venues as safe, transparent, liquid, efficient and providing fair pricing.
Methodology In order to answer the two research questions, the study is carried out in two phases. First, the PDD desktop analysis phase in order to respond our first research question and, second, the case study phase in order to respond our second research question. The first exploratory quantitative phase seeks to have a complete view of the HRM practices informed by the 194-PDD-database as sustainable development co-benefits of Brazilian CDM projects. The second qualitative case study phase seeks to verify whether the HRM practices informed by the PDD were actually being implemented by CDM project proponents. So, a set of 15 case studies have been carried out by triangulation among in-field observation, PDD desktop analysis, and interviews. These data triangulation used in the 15 case studies was important to fulfill the limitations associated with the 15 PDD desktop analyses  (Yin, 2008; Subbarao and Lloyd, 2011).
PDD desktop analysis To carry out the PDD desktop analysis, a database for the 194 registered[3] CDM projects in Brazil (as of September 30, 2011) was created. Identifying registered CDM projects was relatively straightforward as they are publically available and classified by host country in the CDM pipeline of the UNEP Risoe Centre  (Fenhann, 2011). The publically available documents normally used for CDM assessment are the PDD  [4]. As explained earlier, Brazil also has the so-called Annex III documents available and so they were considered for this first phase of research as well. The database created encompasses the key project details extracted from the PDD and Annex III documents, regarding the HRM practices as sustainable development co-benefits of these CDM projects. The collected data were tabulated in Microsoft Excel (TM).
The database was analyzed project by project, using the indicators marked by asterisks in Table I to obtain the percentages of CDM projects that mentioned some improvement in each of the HRM practices examined. Assessing the statements from various sections of the documents could involve some subjectivity, because different analysts and assessment procedures may assign different indicators to a given project. To maintain as much consistency as possible, a single analyst assessed and assigned indicators for all projects. This was previously done in other studies based on a PDD desktop analysis (e. g. UNFCCC, 2011; Subbarao and Lloyd, 2011).
For all the 15 case studies, primary data were collected through semi-structured interviews and field observations. These primary data were triangulated with the secondary data from the 15 PDD and Annex III desktop analysis. The secondary data for this second research phase were extracted from the 194 PDD and Annex III desktop analysis developed to the first research phase. More than one person was interviewed for each company, with the aim of collecting different perceptions of the implementation of HRM practices related to the projects. By the end, 31 people were interviewed, including directors, managers, and employees. The semi-structured interviews displayed precise questions focused on indicators from Table  I. The 31 interviewees were asked to compare the company's HRM practices before and after implementing the CDM project.
Ventura et al. (2012)2)nd empirically applied byFernández et al. (2012)andVentura et al. (2012). The S&E encompasses most indicators used by other studies to assess CDM (e. g. Olsen  and Fenhann, 2008; UNFCCC, 2011; Subbarao and Lloyd, 2011). To select the indicators from the S&E that suit Jabbour and Santos's adapted model for the CDM project analysis, a 194-CDM-project database was analyzed to empirically identify which indicators Information on failures or successes of the mitigation climate change activity is systematized and distributed Organizational culture I15. Employees are stimulated to acquire new values and behaviors related to climate protection Source: Adapted fromJabbour and Santos (2008).
Ventura et al. (2012)2)nd empirically applied byFernández et al. (2012)andVentura et al. (2012). The S&E encompasses most indicators used by other studies to assess CDM (e. g. Olsen  and Fenhann, 2008; UNFCCC, 2011; Subbarao and Lloyd, 2011). To select the indicators from the S&E that suit Jabbour and Santos's adapted model for the CDM project analysis, a 194-CDM-project database was analyzed to empirically identify which indicators Information on failures or successes of the mitigation climate change activity is systematized and distributed Organizational culture I15. Employees are stimulated to acquire new values and behaviors related to climate protection Source: Adapted fromJabbour and Santos (2008) Practices Indicators Recruitment and *I1. Generation of green jobs (directly or indirectly) selection *I2.
Data Collection The TS-115 incident was registered and understood from different perspectives and versions, such as from the company, regulator (ANLA), and media. It was subsequently subject to a rigorous scrutiny of every ex-post message conveyed by the company involved about its surrounding circumstances. Those messages, which for the sake oh this study we consider SRs, were collected from several and accredited on-line public sources oriented to different audiences (i. e. company's stakeholders). In this sense, annual SRs, press releases, internal communications, professional presentations, governmental proceedings and media items (most of them translated from Spanish into English) generated up 3 years after the incident were extensively analyzed. A total of 109 SRs were examined, classified and reduced to 19 workable SRs in which one particular variable were explicitly mentioned and assessed by the company.
5). The Abruzzo Region boasts a long tradition in the field of pastoralism, and has a high appreciation by the consumers. Nevertheless, the region is a net importer of live sheep and goats and represents nearly 20% of total Italian purchases of this product. The census data of 2010 reveal a dichotomous structure of sheep breeding: on the one hand, small and unspecialized farms (with very few animals per holding), and on the other hand, large farms with a number of animals in line with the national context. In order to analyse the organizational arrangements, data have been collected through a direct and structured survey (developed in 2014, see Appendix A). The survey was conducted through a questionnaire organized into four parts: general information (number of heads, rearing, autochthonous breeds, use of transhumance, etc.
Acknowledgments: Authors would like to thank  Mrinal Ghosh  for very useful suggestions. This work is part of the project " Caratterizzazione e miglioramento degli indici salutistici e sicurezza alimentari delle produzioni lattiero ovine tipiche abruzzesi a marchio di origine ," supported by a grant from  Rural Development Plan   2007-2013 (MISURA 1. 2. 4 ) Regione Abruzzo. Project manager  Giuseppe Martino. The authors are grateful to " Associazione Regionale Allevatori d'Abruzzo"  for their kind cooperation.
INTRODUCTION When development of a system is based on results of experiments  (Wohlin et al. , 2012) , the Verification and Validation (V&V) process focuses on measuring different variables, changing treatments and replaying experiments. During these investigations, quantitative and qualitative data is collected that is next analyze statistically to build new information. Knowledge is then deduced from computer-simulated cognitive process or from the transcripts of knowledge acquired by human actors  (Chen et al. , 2009 ). An experimental system has been defined by Rheinberger  (Rheinberger, 1997)  as "a basic unit of experimental activity combining local, technical, instrumental, institutional, social, and epistemic aspects". The process of validating and argumenting on the confidence in an experimental system involves many reasoning elements, such as the way in which the statistical analyses were carried out or the people who took the decisions.
Related Work In  (Basili et al. , 1994) , Basili describes how people in a project organization has to conduct experiences in an empirical way. An Experience Factory (EF) is then an infrastructure designed to support experience management in software organizations. While the initial definition refers to reuse of experiences regarding software products or IT projects, we generalize EF to any information system designed to support experience management. EF supports the collection (Experimentation Software), analysis (Reasoning Software) and packaging of experiences in an Experience Base as shown at the bottom of Figure  2. While EF are interested in capitalizing on knowledge to find solutions to other problems later, we focus in this article on the certification of the approach followed in the analysis process.
Such an argumentation tooling for each step of this process is more and more crucial and strategic for the AXONIC company. ROCKFlows: Automated Construction of Machine Learning Workflows In Machine Learning (ML), data scientists know that the best algorithm is not be the same for each problem  (Wolpert, 1996). Finding a good algorithm depends at least on the size, quality, nature of the data and on what we want to do with the data. According to user dataset and objectives, ROCKFlows aims to generate the most suitable ML workflow, depending on the problem to be solved  (Camillieri et al. , 2016). To tackle this problem, the approach of ROCK-Flows is to build a portfolio  (Leyton-Brown et al. , 2003)  of ML workflows, associated with a Software Product Line (SPL). It is built from the results of experiments and their analysis and helps to find the best algorithms for a task.
For the end user, the workflow that is proposed by the SPL to solve her problem is provided by a black box. However it is still essential to be able to explicit the approach, to validate it, to enable reasoning on it and to expose it in a simple way. In this context, the use of an argumentation solution in ROCKFlows brings benefits to different actors: 1) non expert users who want explanations on workflows ranking in the platform to trust its choices, 2) data-mining experts who want to analyze if the choice of a workflow properly takes in account of the specificities of their problem, 3) contributors who want to understand how the platform is built before extending it by adding datasets, algorithms or improving evaluation strategy, algorithms parameters. Building ROCKFlows' portfolio is a heavy process that involves a high amount of experiments with different datasets, algorithms, parameters, etc.
Objectives Thanks to these uses cases, we identify the three following objectives, we want to achieve. (c1) Incremental, automatic and seamless construction of argumentation diagrams. When data collection and analysis are conducted incrementally (e. g. , adding new steps of experiments analysis in AVEK or adding new algorithms in ROCKFlows), confidence and consequently argumentation may be developed in small increments rather than one large piece. To perform an experiment, several steps have to be taken. According to the granularity of the data collection and analysis, a great number of increments should be taken into account. Consequently, automatic building of argumentation diagrams is needed to tackle the big number of steps. While EFs must be frequently updated and adapted, seamless integration between an EF and argumentation management is essential.
Linking Argumentation Value with the Experiment Process AVEK: an Instrumented Dedicated Tool to Notify Argumentation Factory In AXONIC software ecosystem, the difficulty for us in this context is to deal with the evolutivity of ASF and AVEK. Thanks to the meta-data approach for argumentation elements, we just had to decorate the neurostimulation model of AX-ONIC to support argumentation and send notifications to the event bus. As the analyses are achieved by humans, the argumentation diagram evolves, in particular when business experts add their analyses in the system. ROCKFlows: From a Base of Experiments to an Evolving Argumentation Diagram As the experience base already existed at the time we decided to associate it an argumentation diagram, we began by modeling an abstract argumentation diagram representing the relations between data set, algorithms and experimental results subject to argumentation.
In this example, we deal with only one experiment on a single patient with one stimulation to achieve a goal. This is the minimal case for a study. Scaling issues are high-lighted with this example. This aspect surfaces issues on the evolutivity of argumentation diagrams. How to deal with argumentation steps that are incrementally added to a diagram? What is the impact on the anterior argumentation? ROCKFlows: Evolution Driven Argumentation Diagram Construction The argumentation diagram for this use case is shown in Figure  5. This example shows an argumentation diagram for ranking on accuracy and duration metrics for two algorithms on one dataset. From notifications such as "a new experiment was added", we build a new argumentation step with the strategy execute algo whose inputs are the data used by the experiment and outputs are references to the results.
As the conclusion of this steps needs to be used in already existing steps, the argumentation engine has to check the consistency of the diagram (e. g. , the classify ranking steps must take into account all the measurements resulting from a same evaluation strategy). Today, in ROCKFlows, more than 100 datasets and 60 algorithms are analyzed together and ranked according to different metrics. Thus, the automatic construction of the argumentation diagram helps us to scale the massive argumentation of this use case.
CONCLUSION In this position paper, we propose to automatically build argumentation according to the evolution of an Experience Factory. Through two applications we have shown how the approach, which involves an argumentation factory, can be applied. These two applications consist of surveys of experiments in a bio-medical context and in a portfolio of ML algorithms. In the short term we are going to improve the proposed approach along the following lines. At the level of the argumentation engine we work to take into account argumentation patterns and constraints between them. At the level of the event bus, we are interested in evolving the notion of events to better capture the information coming from experiments using formalisms such as the ExpML language  (Vanschoren et al. , 2012). At the level of the interaction with the argumentation diagrams, we will go through the evaluation phase of ADEV with end-users.
This article addresses the variation of anti-corruption and anti-elite salience in party positioning across Europe. It demonstrates that while anti-corruption salience is primarily related to the (regional) context in which a party operates, anti-elite salience is primarily a function of party ideology. Extreme left and extreme conservative (TAN) parties are significantly more likely to emphasize anti-elite views. Through its use of the new 2014 Chapel Hill Expert Survey wave, this article also introduces the dataset.