dataesr/scientific-paragraphs-categorization huggingface dataset:
To summarize, UNCTAD emerged weakened from the debt battles waged during the 1970s between developed and developing countries. Its objective to promote a mechanism of debt restructuration was defeated. The entangled problematization of debt was also weakened by Group B countries favouring a disentangled perspective in which the predicament of debt referred primarily to domestic problems affecting the borrowing state. Towards the end of the 1970s, UNCTAD operated under growing external pressures to tone down its ²⁵ US diplomacy Database, Wikileaks. Cable sent from the US Embassy in Paris to the US Secretary of  State in Washington DC, 23-12-1977. macro-perspective on institutional reform and focus on developing a technical expertise to assist developing countries in their negotiations with creditors and in their own management of debt.
'In its general aspirations, the NIEO proposal could be seen as an initiative that built upon the Bretton Woods foundations [. ]: long-term international development finance, short-term compensatory financing for commodity export shortfalls, an international debt-restructuring mechanism, backing for infant industry trade protection, commodity price stabilization, the regulation of capital flows, and support for national autonomy in the pursuit of state-led development policies'. The designation of such claims as 'radical' by Group B countries is a consequence of the burying process of the original Bretton Woods foundations. ²⁷ The 'maintenance of debt databases, debt-data validation, day-to-day debt transactions, reporting, debt statistics, operational risk management and basic debt analysis' provided by DMFAS at UNCTAD is part of the "Public debt transparency" agenda pursued by the G20, and the IMF and the World bank, and is considered as a solution role in ensuring effective risk assessment to support sustainable borrowing and lending practices.
We would train developing countries to understand what debt analysis was-what we call debt portfolio analysis-and what they have in their own database, and then be able to do basic debt analysis. And that was in preparation for them, following on with more sophisticated analysis and training from the Bretton Woods Institutions under, principally, the Debt Management Facility. So we became implementing partners to the Debt Management Facility. It was a small part of what we do, but it enabled us to participate in Debt Management Facility activities. ³³ Through DMFAS, UNCTAD became a technical 'partner' of Bretton Woods Institutions (the IMF and World Bank), while being financially compensated for the expertise they supplied. UNCTAD's contribution in this partnership was built around a strictly technical mandate, without trying 'to go into the areas which were demarcated as upstream', emphasizes one expert of DMFAS.
To test these assumptions, we gathered data on sovereign spreads and for two indicators measuring control of corruption and the level of democracy. Our data cover twenty-three emerging market countries for the period 1994-2017. ¹¹ Sovereign spreads, which we collect at quarterly frequency, range between 11 and 7,100 with an average of 474 and a standard deviation of 687 (Table  12. 1). We measure democracy with the 'Level of Democracy' variable from the Quality of Government Database. ¹² This variables ranges between 0 and 10 (with higher values indicating more democratic countries) and is computed as Finally, we measure corruption utilizing the International Country Risk Guide (ICRG) index of control of corruption. ICRG focuses on political corruption and describes its indicator as an 'assessment of corruption within the political system' with a special focus on patronage, nepotism, secret party funding, and ties between politics and business.
Cable sent from the US Secretary of State in Washington DC to the US Mission at OECD in Paris, 10-15-1975. ¹⁸ US diplomacy Database, Wikileaks. Cable sent from the US Secretary of State in Washington DC to the US Delegation in Geneva, 10-31-1975. ¹⁹ US diplomacy Database, Wikileaks. Cable sent from the US Secretary of State in Washington DC to the US Mission at OECD in Paris, 10-15-1975.
²⁰ US diplomacy Database, Wikileaks. Cable sent from the US Delegation in Geneva to the US Secretary of State in Washington DC, 13-05-1974. ²¹ US diplomacy Database, Wikileaks. Cable sent from the US Secretary of State in Washington DC to the US Delegation in Geneva, 10-31-1975. ²² US diplomacy Database, Wikileaks. Cable sent from the US Secretary of State in Washington DC to the US Missions in twenty-five countries, 02-28-1975. ²³ US diplomacy Database, Wikileaks. Cable sent from the US Embassy in Paris to the US Secretary of State in Washington DC and other US Missions abroad, 16-08-1977. ²⁴ US diplomacy Database, Wikileaks. Cable sent from the US Embassy in Paris to the US Secretary of State in Washington DC and other US Missions, 09-03-1976.
Acknowledgements IO system 26, 242, 246, 248,  253 Iraq 203 Irish land bonds 102-3 Irwin, Lord 109-10 Isle of Man 271 Israel 150 , 224 Italy and Mexico 130 military force against Venezuela 30, 49, 64 and Tunisia  79-80 ,  86-87  Jaffe,  J. 28 Jallianwala Bagh  public garden (India) 108 Japan attack on Pearl Harbor (1941) 135 creditors and broken contracts  176-77  and the French Empire 27 introduction 4 and reduction of foreign debts of 21 and resumption of debt service  171-72  Jeanne, O. 309-10 Jecker, Juan 60, 60n. 14  Jersey 271 Jones ,  Charles 57 Jones-Shafroth Act (1917) 308 Jorgensen, E. 123 Joubert ,  M. 82 JPMorgan  and Co. 126, 170, 285, 289  Juarez ,  Benito 60 Junqueras , Oriol 227.
For this reason, preprocessing strategies (under sampling, over sampling) are used to balance unbalanced data sets  (Lin, Tsai, Hu, & Jhang, 2017). These three examples show all the interest of this type of clustering to make predictions according to the belonging to a group. However, these models do not make it possible to follow the possible evolution of a system over time. The prototypical approach is one possible answer. Indeed, a prototype is the result of a compilation of cases (extracted from a database of experiences) which allows to generalize knowledge by avoiding redundant cases and thus to propose typical decision-making schemes  (Bichindaritz, 2015). B. Smyth and P. Cunningham have chosen the CBR paradigm in order to predict the evolution of a performance over time (marathon race)  (Smyth & Cunningham, 2018).
Transposed to our system, the ordered pair (Problem, Solution) corresponds to the ordered pair (U, R) where: U characterizes a sequence of quadruplets (E, A, V, c) and R the couple (system status, recommendation). U is composed of the E entity, where each A attribute has the value V at the c cycle. In the example: ((nurse,fatigue,1. 5,1200),(normal,no preco)), the value 1. 5 is assigned to the attribute fatigue of the agent nurse at cycle 1200. The state of the system is normal and therefore induces no recommendation. Retrieve. This step consists in identifying the source cases such that we can match the target problems with the source problems in order to help in solving the target problem. Specifically, it involves looking for the strongest similarity between a target problem and a source problem from the CBR case database.
As summarized in Fig. 3  the agent nurse does not belong to the case database, but it belongs to a parental (personnel) class. Since both of the agents belong to the same nature, it is therefore possible to associate the collective thresholds inherited from the surgeon class to the nurse class. Review and retain. Each new target case, after having been possibly corrected by experts in the field, is added to the case base. In this section, we have presented our simulation environment for the creation of situations (MAS) and their use in decision help (CBR). Beyond this coupling, the analysis of the data is a key element detailed in the following section.
This is why we have built and indexed our case database according to this principle. The oriented and unlinked graph of Fig. 10  gives an example of possible predictive pathways. It connects clusters two by two between periods of two cycles (cycles c 0 -c 1 to c 1 -c 2 and c 1 -c 2 to c 2 -c 3 ). Each arc is outgoing over one period c l -c l+1 (l ∈ [[0; n -1]]) and incident over the next period. Note that the degree of an incident vertex can be greater than 1, because the predicted status can correspond to two different common status. The Algorithm predict we propose compute the relationships. It is composed of two steps (see Algorithm 2) and the clusters are built according to the Algorithm similVar. It starts (line 1) with an initial cluster cr t (step retrieve of the CBR cycle), where t is the initial cycle.
Evaluation of the similarity with clustering methods In order to evaluate the similarities obtained using clusters, we have run simulations until obtaining stable results. In the context of our study, this number amounts to 10 simulations of 500 cases each. The cases are extracted from our case base, enriched according to the CBR paradigm described in the Section 3. 2. Then, for each case of a real data set (composed of 78 other cases), we searched for the closest cluster based on the variations of attribute values ("FeCO2", "SpO2", "temperature", "fatigue") observed between two cycles. At the end, we compared the cluster's attribute status (normal, SAE) with that of the tested case. Currently, our case base is enriched from the multi-agent system. The example in Fig. 12  represents a set of cases that illustrates the possible states for each of them (normal, failure).
The attributes (temperature, FeCO2, SpO2, PNI, FC) of the agent patient0 chosen for this simulation have a status equal to normal for the cycles from 15 to 25. It is equal to failure in cycle 65. The state of the system is driven by the alert agent which is a synthesis of all the states of all the attributes observed in a given cycle. Any serious undesirable event, resulting from the evolution of one or several attributes, modifies the state of the system whose value becomes failure for the current cycle. The previous states, in antechronological order, take respectively the values: alert3, alert2 and alert1. It is therefore from this type of data set that our clusters are built, the status of which (normal, failure) is then compared with that of a real data set. The results of the different clustering methods are shown in Table  3  and in    compared to the total number of compared cases.
This article presents an analysis of the evolution of international trade in animal products over the period 2000 to 2018, using customs data from the ''BACI" database. Firstly, this article presents the evolution of global trade in animal products for the top five exporting countries (in decreasing order: the European Union (EU), the United States, New Zealand, Brazil and Australia) and then for the two largest importers (China and Japan). It then looks at the world trade situation for four major animal products: poultry meat, pig meat, beef and dairy products. Animal products account for 16% of world agro-food trade; this rate has remained fairly stable throughout the period. The growing imbalance between supply and demand for animal products in Asian countries, particularly in China, is stimulating international trade to the benefit of the major exporting countries.
The term ''animal products" here covers all animal species and all products (raw and processed) derived from them (meat, milk and all other animal products). This analysis is based on statistical data from customs. More precisely, these data are issued from the ''BACI" database developed by the CEPII (Centre français d'étude et de recherche en économie internationale, 2010). The data cover the period 2000 to 2018 (a long-term trajectories). As the last available year is 2018, they do not, however, allow to analyse the effects of the current coronavirus crisis. This article is structured in two parts. The first part presents the evolution of international trade in animal products, focusing on the case of the top five exporting countries (European Union -EU -, the United States, New Zealand, Brazil, Australia) and the top two importing countries (China and Japan).
Data and model availability statement None of the data were deposited in an official repository. BACI data are available on CEPII website: www. CEPII. fr. BACI provides disaggregated data on bilateral trade flows for more than 5000 products and 200 countries. The database is built from data directly reported by each country to the United Nations Statistical Division (Comtrade). The CEPII developed a procedure that reconciles the declarations of the exporter and the importer that may be different in the original data.
| Sample and data collection We draw on Thomson Reuters' ASSET4 database to obtain corporate financial and SP data, an established source of environmental, social, and governance information used for empirical research on CSP  (Cheng, Ioannou, & Serafeim, 2014; Zyglidopoulos et al. , 2016). Initially, we selected the whole universe of companies reported by ASSET4 for the mining and oil and gas industries that respond to the 2-digit SIC codes: (10) "Metal Mining" (165 firms); (  12 ) "Coal Mining" (32 firms); (  13 ) "Oil and Gas Extraction" (173 firms); and (  14 ) "Mining and Quarrying of Nonmetallic Minerals, Except Fuels" (7 firms). Our initial sample consisted of an unbalanced panel of 377 companies observed over the period 2000-2015. To be included in the study, however, firms had to meet the following criteria: (a) they had active physical presence in a country other than the country of incorporation in the form of subsidiaries, which reduced the sample size by 11 firms; and (b) they had to engage in the actual extraction, processing, or marketing, sales, and distribution activities and not solely offer secondary services such as seismic data acquisition and processing, formation evaluation, software and information management, geophysical surveys, and other forms of consulting.
| Control variables At the firm level, we control for multiple other aspects of EIMs' international diversification. We measure a firm's foreign market dependence with its Foreign Sales to Total Sales (FSTS) ratio and international asset dispersion with Foreign Assets to Total Assets (FATA) ratio  (Kwok & Reeb, 2000; Tallman & Li, 1996). Data for FSTS and FATA were collected from ASSET4. Information from the annual reports was also employed to develop Foreign Activity Ownership Type to signify whether a host-country operation was run by a joint venture or a wholly owned subsidiary. Ownership type may alter the attention an operation attracts from host-country constituents and thus shape legitimacy pressures. Finally, we control for the number of different types of activities the EIM undertakes in the same host country in a given year to account for the possibility that legitimacy pressures are influenced by the local scale of an EIM's operations (Host Country Projects).
According to  Chen, Patten, and Roberts (2008) , as a firm matures, its reputation and history of involvement with social responsibility activities can become entrenched. To account for the years of experience of the firm in the industry, we include in the analysis the year of establishment of the firm (Firm Age). We use dummy variables to denote a firm's industry membership (2-digit SIC). At the country level, we use data and measures from several sources to control for potential domestic and host-country contingency factors. First, we obtained data from World Bank's World Development Indicators database to measure a host country's net inflows of foreign direct investment (as a percentage of gross domestic product [GDP]) to capture the conduciveness of a host country environment to foreign companies (Country Openness).
We use the home country's total rents from natural resources (as a percentage of GDP) to account for the economy's munificence in natural resources and its comparative advantage in the extractive industries (Natural Resources Munificence). The corresponding measure for the host country accounts for the host country's attractiveness as a target location and strategic importance to the EIM. Similarly, we measure the (logarithm of the) volume of extracted material outputs (solid, gaseous, and liquid materials) in tons for the home and host countries (Extracted Material) using data from the United Nations Environment Program. We account for the Quality of Environmental Institutions in the home and host countries using the EP index (EPI)  (Hsu, Esty, Levy, de Sherbinin, 2018). EPI ranks countries on 24 performance indicators in categories including air quality, water, heavy metals, and biodiversity.
By taking the absolute value of the difference in Idealpoint between country pairs, we developed a proxy of political distance (Political Distance). Second, we used data from JuriGlobe (University of Ottawa) on the legal systems in the world to develop a dichotomous variable (Legal Distance) that signifies whether the home and host countries share the same legal systems, as an indicator of administrative distance and institutional similarity  (Marano & Kostova, 2016).
| Estimation methods Given the multilevel nature of our data (host countries nested within firms, nested within home countries), we analyze our data using hierarchical linear modeling (HLM). HLM decomposes the total variance in SP and EP responses into between-cluster variance and within-cluster variance, which renders the HLM estimator more efficient than OLS estimators (Rabe-Hesketh & Skrondal, 2012). 7 To enable a 1-year lagged structure for our main independent variables and controls, we dropped from the sample 15 firms that appeared in the sample for only 1 year and 8 more firms because of missing values. Thus, our final sample pertains to 29 countries and 340 EIMs with foreign presence in 158 host countries that total 7,565 observations. Our sample compares favorably with other studies of CSP in multinational settings that use HLM  (Mueller, Hattrup, Spiess, & Lin-Hi, 2012; Zyglidopoulos et al.
Second, the limitations of our dataset, which captures only corporate-level CSP, mean that we have been unable to analyze the potentially complex interactions between challenges to legitimacy arising at the subsidiary and corporate levels and joint optimization of the management of CSP across these different levels. Our results, however, hint that these interactions would be a fruitful topic for future research. Further research might extend our findings by including subsidiary-level CSP responses in the analysis of how MNCs respond to the legitimacy challenges they might face. Finally, while we contribute toward a better understanding of how MNCs deal with their legitimacy challenges, our research only deals with CSP and legitimacy spillovers or challenges in a broad sense. Future research might valuably investigate the link between particular types of legitimacy challenges and particular kinds of CSP.
, 2009)  and MA-CODO  (Weyns et al. , 2010). MACODO, which extends ORA4MAS, is a middleware for contextdriven dynamic agent organization and proposes an abstraction of organization separating coordination and structuring aspect from the local behaviour of agents. Environments represent the software context of communications, perceptions and actions between agents. This approach provides several answers to the issue of the dynamic relationship between agents, organization level and environments, but no explicit concepts to describe the dynamics of organizations. Current organizational approaches do not take into account multi-level aspects, such as the dynamics of environments, the dynamic structuring of groups, and the couplings between these different elements. Modelling a highly structured system requires an explicit representation of interactions between atomic elements and their environments, but also between agents from each level.
g. Knight and White, 2017). Regarding the role of age in these relationships, the meta-analysis of 83 studies provided by  Kooij et al. (2010)  underlines that the positive relationships are strengthened for older ages for flexible work schemes and weakened when HR practices relate to individual career development. Only a few studies have explored the issue of age in relation to turnover intention and HR practices. Stirpe et al. (2017)  assess the link between retention rate and HR practices, with the age structure in the workplace as a moderator. Bal and Dorenbosch (2015)  also used workplace survey data to analyse the age-related differences in the relationship between HR practices and organizational performance, such as absenteeism and turnover. In this article, we propose an analysis at the employee level to investigate age-related differences in the relationship between motivational practices and voluntary turnover.
-INSERT TABLE 2 ABOUT HERE - The estimated marginal effect 7 of the interaction term between motivation-enhancing HR practices and employability is negative and significant (Model 3). The interaction term between flexibility-enhancing HR practices and employability is also significant and negative (Model 4). However, the results in Model 5where all the interaction terms are included - 5 The estimates may suffer from the omitted-variable bias such as personality traits that are likely to be linked to voluntary turnover. Unfortunately, we do not have this information in our dataset. 6 We do not report details on control variables nor discuss related research findings because of space limitations. 7 As the interaction terms in a Probit model are tricky to interpret, we interpret only the significances, not the magnitudes.
A c c e p t e d m a n u s c r i p t Data and methodology Data The data comes from a nationally representative employee survey for Luxembourg conducted in 2013 using the guidelines provided by the MEADOW Consortium (2010). The questionnaire of Working Conditions and Quality of Work Life survey was sent to a stratified random sample of employees ranging from 16 to 65 years of age, who had worked for at least twelve months in any workplace in the private sector with 15 or more employees. The dataset constitutes a large representative sample of 16,896 employees working in the Grand-Duchy of Luxembourg. It covers 68 per cent men and 32 per cent women and representing the active population of Luxembourg's private sector labour market. The employment protection legislation is above the one observed at the Organization for Economic Cooperation and Development (OECD)average and the unemployment rate is around 6 percent.
benedelman. org/docs/htc-mada. pdf, accessed 24 July 2019). 3 The installation process has several steps, including requiring the user to disable a security feature that prevents installation of potentially harmful software and unlawfully downloading Google Play from a third party website. For an installation tutorial, see https://www. androidpolice. com/2020/07/25/install-play-store-guide/, and for EC commentary, see http://europa. eu/rapid/press-release IP-18-4581 en. htm, accessed 19 October 2020. 4 Various observations support the EC's belief. First, evidence submitted to the EC during its investigation (European  Commission, 2018) indicates that competing app stores lack important popular apps from firms such as Netflix, Amazon or Facebook (p. 134); and that consumers are unlikely to buy a device that does not have access to the apps they want (e.