dataesr/scientific-paragraphs-categorization huggingface dataset:
Research in context Evidence before this study Health care, as any economic sector, relies on physical stocks and flows. The carbon emissions associated with health care have been assessed both at the regional and the global scale. The PubMed database was explored from inception to June 1, 2022, for studies in English, using the keywords "healthcare" or "health care" with either "energy footprint", "raw materials", "environmental footprint" or "resource footprint". After reviewing the titles, a single study assessed the scarce water use associated with health care globally, together with other environmental impacts. Two studies have assessed the resource footprint of the health-care sector in the Netherlands and Germany. To the best of our knowledge, no study has thus far estimated the resource footprint of health-care systems at a multiregional level, apart from scarce water use.
Added value of this study This study advances existing research by assessing more than 1000 environmental and social impacts of health-care systems, across the whole value chain, including infrastructures. The added value of our methods compared with previous work is; the harmonisation of the expenditure data with WHO reference data, the endogenisation of capital in input-output calculations, the correction of purchasing power parity for the analysis of intensities, and the comparison of the results with health-care access and quality (HAQ) indexes. Among the impacts studied, resource footprints (divided into non-metallic minerals, metal ores, and fossil fuels) and dependencies were assessed for the first time. Energy footprint was used as an indicator representative of all resource footprints.
Implications of all the available evidence Low-income and middle-income countries not only have lower HAQ indexes compared with high-income countries, but also suffer from some of the impacts associated with the high HAQ indexes of rich countries. In a world with limited access to low-carbon energy sources, it is crucial to limit the growth of the global health-care energy demand. The most effective compromise would be to target the reduction of health-care footprints in high-income countries to ensure fair access to high-quality health-care systems in low-income and middle-income countries. This objective could be reached by a combination of sociotechnical interventions and prevention. See Online for appendix 1 done with other input-output databases. Input-output analysis is a top-down approach to trace the worldwide industrial output necessary to produce the final consumption of a given product in a given region.
25 nvironmental extensions link the factors of production, such as resource footprint or environmental impacts, to the industrial output. Thus tracing the resource footprint of a specific sector of final consumption, such as health, is possible. Resource footprints in a given region can be disaggregated by region of production to calculate a share of resource imports. To have a common and trustworthy source for health expenditure data, we used WHO's Global Health Expenditure database  26  and completed it with the Organisation for Economic Co-operation and Development's health expenditure database. 27 Health expenditures were mapped to six EXIOBASE sectors (appendix 1 pp 2-3): health and social work services (85); chemicals nec (not elsewhere classified); retail trade services, except of motor vehicles and motorcycles; repair services of personal and household goods (52); insurance and pension funding services, except compulsory social security services (66); public administration and defence services; compulsory social security services (75); and private households with employed persons (95).
The numbers between parentheses are given by EXIOBASE only for some sectors and not for others, without further explanation of what they correspond to (appendix 1 pp 3-4 for the equations used in the calculation process). A limit of classic input-output databases is that capital formation (ie, investments) is reported as final consumption and not as intermediate consumption. For example, the cement and steel required to build a hospital is not accounted for in health and social work services but in construction work. The Global Health Expenditure database provides capital health expenditure for some years and some regions, but data are neither complete nor disaggregated in capital sub-sectors. 26 To overcome this limit, we have endogenised capital use in EXIOBASE3 using capital use matrixes.
Resource footprints and dependency Once EXIOBASE3 was modified to account for capital use, we calculated resource footprints, measured in tonnes (1000 kg) and tons per capita. Non-metallic minerals, metal ores, and fossil fuels were distinguished because they are associated with different environmental impacts and supply risks. Tracing footprints also allowed us to assess resource dependencies, defined as the percentage of imported resources. Resources data ended in 2013. 30 ithin the three resource categories, only fossil fuels  17  and some metallic ores  31  were found to be crucial, whereas other metallic ores and non-metallic minerals were not. The dependencies are still calculated for each category, as it also gives insights to where resources are produced and thus to the offshoring of the associated impacts.
The final index is a score from 0 to 100 per country per year, constructed from the 32 causes studied. 34 For the five rest of the world EXIOBASE3 regions, we have aggregated this index using population-weighted means. We performed a panel data analysis to understand the relationship between HAQ and energy footprint per capita of health care systems. Panel data analysis allows us to study data that is measured for the same objects (here regions) at different points in time. We chose the natural logarithm of the energy footprint per capita as the dependent variable and the HAQ index as the independent variable. We carried out a preliminary Hausmann test to choose the regression model. The result, giving a p value of 0•0004 proved that the individual characteristics were correlated with the regressor and that the fixed-effect model was more appropriate than the random-effect.
It makes sense as there are reasons to think that cultural or political characteristics of the various regions affect the HAQ index. We used the Python Statsmodel module to carry out the analysis. As we found significant differences in energy footprints for countries with a similar HAQ index, we disaggregated the energy footprint of the ten countries with the highest HAQ index. We compared the energy used directly by health-care sectors with that used for chemical production, production of other raw materials, transport, equipment, construction, food, energy production, and other for all other EXIOBASE sectors.
Health-care systems efficiency Energy efficiency is defined as the inverse of energy intensity (energy use per unit of expenditure). To compare intensities between regions and across time, expenditures must be corrected to account for purchasing power parity (PPP) and inflation. One US$PPP buys the same basket of goods in any region, whereas one US$ buys goods according to market exchange rates. However, $PPP data do not account for inflation. Consumer Price Indexes account for inflation across time in a given country. We used expenditure data in $PPP from the Global Health Expenditure database and corrected them using US health care Consumer Price Indexes for the years 2002-15, previous years not being available. 35 hanges in the obtained energy intensity can be explained by actual efficiency gains (ie, using less energy to perform the same task) but also by changes in the input structure of the sector, for example of the cost of employment, which corresponds to zero energy input.
This is confirmed by the panel data analysis, for which the R² between regions (0•73) is twice higher than the R² within regions (0•36; table). Some countries, such as the USA or Switzerland, underperformed whereas others, such as Spain or Croatia, overperformed (appendix 1 pp 14-16). During the 1995-2015 period, the HAQ indexes increased in all 49 EXIOBASE3 regions. However, five regions-India, rest of the world Africa, Indonesia, South Africa, and rest of the world Asia and Pacific-representing half of the world's population, still had HAQ indexes less than 55 in 2015. High-income countries not only have lower HAQ indexes than low-income and middle-income countries but also suffer from some of the impacts associated with the high HAQ indexes of rich countries. For example, 64% of the impacts associated with health-care-related non-ferrous metal ore extraction takes place in the lowincome and middle-income countries without China, though this region's consumption is responsible for only 19% of the impacts (appendix 1 p 9).
As no other study has assessed the global energy footprint of health-care systems, we compare our greenhouse gas emissions results with previous studies. Our results for CO 2 emissions in 2014 were similar to the previous multiregional estimates using the Eora EE-MRIO database 9 (appendix 1 p 24). Our results were also close to the values reported for 2015 in the Health Care Without Harm, 10 which used the Global Trade Analysis Project database to assess health-care systems' climate footprint in 43 countries (appendix 1 p 25). In contrast, our results for greenhouse gases in 2015 differed significantly from those of Lenzen and colleagues 1 (appendix 1 p 26). Unlike the other two multiregional studies, they used the health-care-related sectors directly from Eora, without harmonising the expenses with WHO's Global Health Expenditure database.
The main limitations of this study are those inherent to input-output modelling, in particular to the raw data used to build environmental extensions. EXIOBASE3 does not provide measurement errors so it was not possible to propagate the uncertainties as can be done with other input-output databases. However, a previous study on the environmental footprints of health-care systems 1 used Monte-Carlo techniques to assess the uncertainty of their results from the SDs provided by the Eora database. They found final SDs of less than 5%. 1  When comparing regions, we found that exponential amounts of resources were required to support the development of health access and quality systems. As the environmental impacts associated with these resources are responsible for an increase in some non-communicable diseases, health systems find themselves in the midst of a vicious cycle.
The indicators will form the basis to operationalize PRUDENT's monitoring framework. • EU-wide monitoring framework for oral health systems performance: Based on the identified performance indicators and essential items for oral care (see above), PRUDENT will develop a multicountry monitoring framework for oral health systems performance. Available data sources with relevant oral health system information will be mapped. PRUDENT will also carry out an online survey of EU citizens' perception of oral health systems performance in select countries. The information from the citizen survey and existing data sources will be incorporated in a cross-country interactive dashboard. Objective 2: In-Depth Analysis of Optimization Strategies for Oral Care Financing • Behavioral economics lab to decipher the impacts of incentives on providers' and citizens' choices: To improve the understanding of demandand supply-side behavioral mechanisms in the context of oral care markets, PRUDENT builds on state-of the-art stated preference methods to generate an in-depth understanding of how providers/citizens respond to incentives and infrastructures of oral care markets.
Discrete choice experiments will serve to identify citizens' preferences for different dimensions of oral health (particularly oral health-related quality of life), the impact of potential barriers to demand (e. g. , out-of-pocket payments), and personal characteristics such as risk aversion and time preferences. Provider preferences will be elicited to decipher their extrinsic and intrinsic motives. In addition, linkage of data from the discrete choice experiments with registry data from Denmark will allow comparisons of real-life choices with stated preferences. SES vs. low SES). The new payment system (= intervention) aims to reduce social inequalities in children/adolescent oral care. To compare the intervention group with the current standard of care (= fee for service), a synthetic control group will be constructed based on routine health insurance data.
2 The research could benefit from the excellent knowledge and awareness of cultural policies in the urban domain of the various local stakeholders involved in preparing this work. 3 The City of Issy provided the geographic database through the "DATAISSY. COM" portal. Additional data was obtained from institutional public databases or private ones. 4  This part of research introduces the main features of the Issy territory, mainly investigating the inhabitants' social profile in "Introducing the city of Issyles-Moulineaux" Section. The in-depth study of cultural policies related to the urban domain has been supported by rich technical documentation pertaining to policies and projects described in "Cultural policies in Issy-les-Moulineaux"and "Cultural policies and the urban domain: three different levels of observation" sections.
"Coca-Cola, Microsoft, Accor Hotels, Sodexo, La Poste, Icade, Orange. At the southwest gates of the capital, Issy-les-Moulineaux is always attractive for the headquarters of large international companies. " Le Parisien, 6 June 2016 (translation of the authors) With over 720 businesses/artisans and 800 active businesses, more than 50% of which are in the communication and information technology sectors, the City has created an efficient economic framework. Since the mid-90s, Issy has embarked on information and communication technologies and has become a recognised reference in this field both in France and abroad. "The Ile-de-France Region has launched the platform "Ile de France Smart Services". Issy-les-Moulineaux has been one of the first Cities to have signed this agreement. The implementation of these services is the result of an unprecedented partnership approach around data between public and private actors, as it also represents a common data portal including datasets and data visualisation tools"  (Acar et al.
Acknowledgements This article is developed and funded under the umbrella of the  MESOC Project. MESOC (Measuring the Societal Value of Culture)  Project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 870935. (More info at: https:// mesoc proje ct. eu/) The opinions expressed herein are solely by the authors and do not reflect the official point of any EU institutions or any other member of the  MESOC Consortium. Esther Destrés interacts with the MESOC project for Relais Culture Europe (www. relais-cultu re-europe. eu) provides links to the data set, websites, articles and documentation about Issy-les-Moulineaux; moreover, she interacts with the authors and facilitates the interview with a local stakeholder. CLAVIM Association (https:// www.
The goals of the WSs were to identify the general aim of the research work, share bibliography and theoretical references (1st WS), build a solid shared database of initiative, policies and data about the case study (2nd WS), identify the primary outcomes and discussion about the core idea of the paper, like the role of cultural policies as a vehicle for socio-spatial transition (3rd WS), carry out further in-depth research on some specific policies developed by the City (4th WS), verify some hypothesis and conclusions with an external expert (5th WS).
Acknowledgements. This publication is supported by the  J. S. McDonnell Foundation   Understanding Human Cognition Scholar Award  to AC. The content is solely the responsibility of the authors and does not necessarily represent the official views of the J. S. MF. We thank  HoJin Kim  and  Maria Diehl  for their assistance with data collection and vowel extraction. We thank the mothers and their infants for participating in this study and the  Montgomery County Animal Care and Adoption Center  for allowing us to employ two puppies from their facility.
Background: The impact of social movements (SMs) and collective behavior (CB) supports the relevance of approaching this phenomenon from social psychology. Several systematic reviews ( ) and meta-analyses ( ) have been carried out in the st century, but there is a lack of integration. Aim: This study seeks to review the patterns of CB and corroborate the psychosocial factors that explain participation in CB and SMs, as well as the long-term psychological e ects of participating in them. Method: A systematic search was carried out in the databases Web of Science, Scopus, ProQuest, ScienceDirect, Willey Online Library, EBSCO, and JSTOR for articles dated between and. We searched for meta-analyses and systematic reviews that empirically evaluated social movements and collective behavior. Of the initial records, after scanning and eligibility phases, meta-analyses and systematic reviews were analyzed in the present work.
, 2017; see Pizarro et al. , 2022 in this monograph). This study examines meta-analyses, systematic reviews, and specific studies that exist to date on the main variables and their correlates with the objectives of (1) reviewing common patterns of behavior, emotion, and cognition during CB, (2) psychosocial factors that explain participation in SMs and CB, and (3) the medium-and long-term psychological effects of participation in them. This is, to our knowledge, the first text that integrates the classical observational literature on collective behavior, with the studies of social movements. In addition, also in a novel way to our understanding, studies on the collective behavior of all kinds are combined, not limited to the manifestations of political protests. It is also the first article to carry out an integration of meta-analysis, estimating a weighted average effect size for each psychosocial factor of the SMs, generally based on three pooled databases.
Eligibility criteria Meta-analyses and systematic and specific reviews were included that described (a) studies on CB and SMs; (b) there was no limitation of age or type of CB or SMs; and (c) works published in Spanish, English, French, Portuguese, and Italian. Concerning the exclusion criteria, the following were excluded: (a) those that were individual or single-case studies; (b) documents that were not peer-reviewed publications, including doctoral theses or gray literature; (c) those written in languages other than the five designated for selection; and (d) documents for which the full text was not available, nor the corresponding databases, nor through professional networks of academic personnel (e. g. , "Google Scholar, " "ResearchGate, " or "Academia Edu").
Data sources and search For the systematic search of the scientific literature, the following databases were consulted: Web of Science, Scopus, ProQuest, Science Direct, Willey Online Library, EBSCO, and JSTOR. The key words "Collective Behavior, " "Social Psychology, " "Social Movements, " "Meta-Analysis, " and "Systematic Review" (as well as their equivalents in the search languages) were used in the search strategies. Those terms were searched in combination using the Boolean connector "AND" and "OR", specifying field category "Social Psychology" AND "Social Movements" AND "Meta-Analysis" OR "Systematic Review"; "Social Psychology" AND "Collective Behavior" AND "Meta-Analysis" OR "Systematic Review", and with temporal limitation of publication. Terms such as collective action and political action were not included because we wanted to maintain the historical tradition of referring to collective behaviors and social movements-as defined in the classic chapter by Milgram and Toch in the Handbook of social psychology of 1969.
Quality assessment Once it was ascertained that the articles examined the patterns of CB, motivational factors for participation in SMs and CB, and the psychological effects of participation in them, the full content of the articles was analyzed to see to what extent they met the quality criteria. The articles finally selected were published in peerreviewed journals or chapters in books recognized for their quality (Handbook, Annual Review) and were meta-analyses, systematic narrative reviews of evidence-based studies, integrative reviews of theories on SMs, and classical reviews on CB. The information was coded in Excel for subsequent analysis and discussion. The chosen criteria were: (a) type of document, (b) authorship, (c) year of publication, (d) title, (e) journal or book in which the results were published, (f) country in which the research was conducted, (g) objectives of the study, (h) study design, (i) sample size and composition (N and/or K), (j) data collection instruments, (k) language of publication, and (l) main results.
Acknowledgments We thank  DP  for his invaluable help in this work. In a year in which his retirement is imminent, we consider that this article in the framework of the monograph honors so many years of work dedicated to this line of research and the legacy that his work has left behind. In this context, we would also like to thank the many other giant arms that have inspired DP, such as  Professor Bernard Rimé ,  Professor Nekane Basabe , and  Professor Federico Javaloy †  , who have allowed him to bequeath his wisdom on this exciting subject to new generations of researchers and to those of us who have recently been introduced to it.
Public Procurement refers to governments' purchasing activities of goods, services, and construction of public works. In the European Union (EU), it is an essential sector, corresponding to 15% of the GDP. EU public procurement generates large amounts of data, because award notices related to contracts exceeding a predefined threshold must be published on the TED (EU's official journal). Under the framework of the DeCoMaP project, which aims at leveraging such data in order to predict fraud in public procurement, we constitute the FOPPA (French Open Public Procurement Award notices) database. It contains the description of 1,380,965 lots obtained from the TED, covering the 2010-2020 period for France. We detect a number of substantial issues in these data, and propose a set of automated and semi-automated methods to solve them and produce a usable database.
It involves an interdisciplinary team that includes academic researchers in Economics, Law, and Computer Science, as well as operators of the open data industry. The project, which is funded by the ANR (French national research agency) under grant number ANR-19-CE38-0004, started in 2019 and will end in 2024. It mainly has three goals: 1) constitute a database of public procurement notices and perform its descriptive analysis from the perspective of both Economics and Law; 2) leverage this database to propose an automatic and explainable method able of detecting fraud in public procurement; 3) take advantage of the descriptive analysis and the explainability of the detection method to provide the public government with guidelines regarding the improvement of public procurement open data. At the first stage of the DeCoMaP project, we constituted a database of French public procurement notices, whose description is the object of the present article.