dataesr/scientific-paragraphs-categorization huggingface dataset:
2013). 7 Regarding political institutions, we create a binary indicator of political regimes based on the Polity2 index from the PolityIV database of Marshall and Jaggers (2010). Consistent with the classification of Przeworski et al. (2000), Persson and Tabellini (2003), Persson (2005), Giavazzi and Tabellini (2005), and Acemoglu et al. (.
We are indebted to the Editor ( Sushanta Mallick ), and to two anonymous referees for comments on a previous version. We benefited from many stimulating discussions with members of the  INFER (International Network For Economic Research) , and in particular with  Cristina Badarau ,  Jean-Louis Combes ,  Camelia Turcu  and  Marcel Voia. We thank the  ANR (Agence Nationale de la Recherche)  for their financial support through the  "Grand Emprunt " and the  LABEX IDGM+  ( ANR-10-LABX-14-01 ) mechanism. Usual disclaimers apply.
( 2013). 7 Regarding political institutions, we create a binary indicator of political regimes based on the Polity2 index from the PolityIV database of Marshall and Jaggers (2010). Consistent with the classification of Przeworski et al. (2000), Persson and Tabellini (2003), Persson (2005), Giavazzi and Tabellini (2005), and Acemoglu et al. (.
In this paper, we develop a price advantage measure obtained as the difference between efficiency scores computed with quantity-based and value-based data. This measure is interpreted as an increase in the firm's profit rate due to a favourable input and output price environment. Compared to traditional allocative efficiency measure, which uses only the evaluated firm's prices, price advantage measure considers prices of peers in the market. Our main contribution is to show that total-price advantage decomposes into the sum of output-and input-specific price advantages. The application of the measure to a French farm's dataset in the context of the successive common agricultural policy reforms -aiming at the liberalization of agricultural prices (1992-2013) -illustrates our theoretical model.
Moreover, our indicator is more general as it also accounts for output prices (profit setting) and proposes a decomposition of total price advantages into output-and input-specific price effects. We take advantage from the context of prices liberalization and reinforcement of farmers' incentives to consider price signals in their decisions (from common agricultural policy, CAP hereafter, reforms) to show the relevance of our price advantage measure. Additionally, through the decomposition of total price advantages, we are able to characterize the origin of price advantages for each produce. This information is valuable for both practitioners and public authorities in markets ongoing a deregulation process. Specifically, we used a dataset of 11,967 observations from 1,186 farms located in the French department of Meuse for the period 1992-2013.
Meuse sample Agricultural activity in the Meuse department predominantly involves both crop farming and animal husbandry (Ecoscopie de la  Meuse 2000; 2010; 2012). The main contributors to the total agricultural revenue by share are: crops (except fodder), milk and dairy, and cattle and calves. Moreover, over the last 40 years, the number of crop-specialized farms increased while those specializing in both crops and animal husbandry, and those specializing in bovines (milk, meat, or both) and sheep decreased. Further, the size of the farms increased steadily, consistent with the general trend in France. Our dataset of farms observed in Meuse reflected these changes. Panel data from 1,186 farms, with 11,967 observations were available for the period 1992-2013. 5 The unbalanced panel comprises farms with an average age of 10 years.
For this, we defined an output-oriented directional distance function in the output-quantity space ( ) Table  2  presents the mean values and trends of the outputs (constant 2010 prices=100) and inputs. As one can notice, the main source for revenue for an average farm in our dataset is crops, followed by milk and bovine meat. While the trend for each of these productions is positive, it is strongest for bovine meat revenue. At the same time, one notices that the time trend has been positive for all inputs except labour. The fastest growing trend is for intermediate inputs. Note. Trends were estimated using a linear regression between the variable expressed in log and time. , ; 0, We estimate LP 4-6 for each year separately. Thus, we dispose of yearly estimations for their technical, value and revenue inefficiency.
Allocative inefficiency and price advantages: a joint analysis Figure  1  shows that allocative inefficiency for DMUs in our dataset ranges 3 -6%. 6 Based on this indicator, one is tempted to say that farms in our dataset are particularly efficient with regards to their relative price system. Furthermore, the proportion of allocative efficient farms varies from 38. 9% (in 1994) to 59. 3% (in 2013) and was 47. 4% for the whole period (1992-2013). However, allocative inefficiency is computed given the observed set of prices for each DMU and does not include any inter-DMU comparison of prices. On the other hand, price advantage is calculated by comparing the set of prices and outputs between DMUs. Moreover, our findings on allocative inefficiency seem to contrast the graph for price advantage.
However, two opposing effects occurred. The proportion of allocative-efficient but suffering from an unfavourable price environment increased, while the proportion of allocative-efficient farms enjoying a favourable price environment decreased. The first evolution was stronger than the latter. ii) There was a decrease in the proportion of farms enjoying a positive price advantage, regardless of whether they were allocative efficient or inefficient. 8 If we consider that farms need to optimize their production decisions with respect to their own set of prices (allocative efficiency), the results suggest that the farms in our dataset performed well.
7 The correlation coefficient of 0. 03 was not significant at <5%. 8 The proportion of farms that are neutral from their price impact perspective is constant over time. However, if we compare prices across farms, results are more mitigated since some performed very well, while others performed very poorly. Moreover, allocative inefficiency assumes that farms modify their input/output mixes, which is an acceptable assumption for mid-to long-run analysis. However, one can expect that for short-run analysis, farms operate with a given mix and thus, our measure of price advantage (or price environment), makes sense. The rest of this article is dedicated to an analysis of the price advantages for farms in our dataset. These findings are reinforced by the period trends presented in table 4 below.
Decomposition of the total-price advantage into output-specific price advantages Table  6  presents the percentage of farms producing a specific output and the valence of their price advantages as per the algebraic decomposition. Over the analysis period, two contrasted effects can be emphasized: the increase in the proportion of farms evolving in positive milkspecific price environments and the decrease in the proportion of the farms performing in cropspecific positive price environments. Figure  3  shows the effect of the 2003 reform on the relative distribution of farms, taking into account that this reform attempted to decouple aids received for the crop production. Our indicator shows that while farms in our dataset used to extract their price advantages from crop production before the reform, they used milk production to obtain their price advantages postreform.
While some farms used the benefits of new price opportunities, most did not and suffered from price disadvantages. This could imply that due to the Reform, the latter lost the price rents they used to have on their produces. However, if we base our analysis on the concept of allocative efficiency only, we would be tempted to conclude that for the farms in our dataset, their efficiency slightly increased with time. Thus, one cannot conclude in favour of a possible association between this observed evolution and the 2003 MTR. Finally, given the decomposition of the total price advantage as the sum of output-specific price effects we observed a decrease in the crop price advantages after the adoption of the 2003 MTR. This phenomenon was accompanied by an increase in the milk price advantage and a stable level for the bovine-meat price effects.
Sample To reach the objective proposed in this study we used the European SMEs and the Circular Economy database, which is based on Flash Eurobarometer Survey number 441  [40]. The survey was commissioned by the European Commission to explore SMEs circular economy activities following the ambitious circular economy package which was adopted in December 2015  [5]. The survey was carried out by TNS Political & Social network in the 28 Member States of the European Union between the 18th and 27th of April 2016. Therefore, this database takes information from the year 2015 on CE activities implemented in companies in European countries, as well as the barriers to these CE practices. Thus, our sample consists of 10,618 companies in 28 EU countries. The sample focuses on SMEs, as shown in Table  1.
, 2003). As such, downgrading represents not only a loss of legitimacy for the restaurant but could also influence consumers' judgement, since stars represent the most valued criterion for all agents in the field  (Karpik, 2000; Gomez & Bouty, 2011). In this context, we seek to explore how a negative evaluation, made by an expert, influences consumers' judgment on legitimacy. To do this, we analyze the 665 online reviews of the 'Relais Bernard Loiseau' posted by the consumers on the TripAdvisor website, from 2004 to June 2017, through a mixed method based on quantitative data (regression and analysis of variance) and qualitative data (correspondence analysis, automatic textual analysis and content analysis). Our findings reveal a null effect on the rating assessed by consumers and a non-linear influence on the online comments that evolve between the short and long term with the emergence of new criteria of evaluation, combined to a great confusion between them, an increase and then decrease of negative emotions.
Data collection Exploring our issue requires an empirical setting in which consumers' perceptions are available for a long period to study their potential evolution between before and after the expert negative judgment. As the most commonly used by consumers in Europe and in France in particular  (Ayeh, Au & Law, 2013; Zaman, Botti & Thanh, 2016) , the TripAdvisor platform meets our requirements and allows to collect rich qualitative and quantitative data, which are both spontaneous and independent (e. g. ,  Zaman et al. , 2016). On this platform, consumer reviews are based on an algorithm and a form designed by TripAdvisor, including an overall five-items scale rating and ratings of different components of experience: quality-price ratio, location, cleanliness and service, as well as textual comments and photos.
TripAdvisor encourages consumers to report on their experience by writing a free textual comment. Additional and optional information about consumers is also available, such as age, gender, geographical origin, number of emitted posts, date of issue and date of stay. Our dataset represents all the 665 online reviews posted on this website since the first publication in August 2004. As all reviews provide ratings and online comments (data analyzed), we collected all of them. We ended our data collection in June 2017 with the launch of a new menu, and a new adapted pricing scheme. As 70% of the online reviews come from French consumers, we first used the translation service of the TripAdvisor website for the 30% of comments written in foreign languages, as such translations are the ones read by French consumers.
We followed four steps of analysis. First, we explored the influence of this negative expert judgment on the rating through two analyzes (quantitative data). To explain the global rating and, particularly, which factors (among the four items initially evaluated by consumers on TripAdvisor) contribute the most, a multinomial logistic regression was first conducted (Appendix 1). This led us to keep three items (quality-price ratio, cleanliness and service) for the ANOVA (Table  2 ). We then performed a content analysis of the comments (qualitative data), through different steps. In the second step, to observe the potential evolution of criteria on which consumers assess the restaurant, we used a correspondence analysis via a descendent hierarchical classification for each period. The aim of the algorithm is to order the textual data source through a process of categorization and reduction.
It was then possible to interpret the distance in space between words (and by extension between classes of words). To do so, we used a qualitative module, IRAMUTEQ, of R platform (step 2). In the third step, we attempted to reveal the potential evolution of the emotions contained in consumers' comments. To achieve it, we used a text-analysis software, Linguistic Inquiry and Word Count (LIWC), to categorize multiple dimensions of language  (Humphreys & Wang, 2017; Wang et al. , 2016). LIWC searches, counts and codes words according to its own dictionary. Among the 80 categories available, related to our research question, we considered the following ones: anger, sadness, negative emotion, causation, tentative, inhibition and assent. LIWC program calculates the percentage of total words contained in each comment that match with those categories.
This represents an inherent limitation, which has been nevertheless reduced when interviewing the organization members and ten consumers and by the fact that in the correspondence analysis, this cluster still remains distinct from the other criteria in the online comments. Second, despite we observed that the profile variables of consumers have no direct effect on the global rating (reviewer's gender: p=0. 585, issuer's age: p=0. 681; reviewer's geographical origin (French or not): p=0. 741), it could be interesting to take into consideration the status of consumers, and notably the distinction between French and foreign consumers, in the way they perceive the legitimacy of this restaurant. Another limitation relies on the use of a unique website, TripAdvisor. Despite we collected all the online reviews available on this website and conducted ten interviews to limit sampling bias, a research perspective could consist in collecting data on other foodspecialized websites.
Acknowledgements The authors contributed equally to this research. We would like to thank  Ashlee Humphreys  and the anonymous reviewers for their helpful suggestions, guidance and encouragements. For their comments on previous versions of this paper, we would like to thank  Karim Ben Slimane ,  Bernard Forgues  and  Bernard Leca. We are also grateful to the participants at the  AIMS, AFM  and EGOS conferences in 2018 and the 'Bringing institutional theory to marketing' workshop in 2017 for their feedbacks. All errors are our own. *Corresponding author :  Helene Delacour -University of Lorraine -IAE School of Management -13 rue Michel Ney -54 000 Nancy -France -helene. delacour@univ-lorraine. fr -+ 33 6  62 12 02 88.
Sampling and data collection Given that the objective of this study is to examine the antecedents and consequences of hosts' trust in the Airbnb context, we collected primary data only from Airbnb users who are registered on Airbnb as hosts. We identified the active Airbnb hosts from five closed groups on Facebook, which together include more than 4,500 hosts who share their rooms/flats/houses on Airbnb in the United Kingdom, Japan, Australia, and Canada. Data were collected using host community member lists. We randomly selected 300 members from each host community as potential participants, and used the Facebook Message System to distribute the survey, along with an information letter containing a description of the research purpose and an information privacy protection statement. The survey lasted 8 weeks from March to May 2017.
This ontology allows multi-criteria querying associating the nutritional and sensory quality of the food product, the transformation process constraints, and the environmental impact of the whole system. As a pioneering example, the BaGaTel database already aggregates data imported from collaborative projects conducted on dairy foods, covering technological processes with their environmental impact (hard cheese, yogurt), food composition and structure, sensory properties, and food bolus properties, all connecting with human oral physiology and nutritional properties (http://plasticnet. grignon. inra. fr/PortailNutriSensAl/). It was built using the PO 2 ontology  [39]. PO 2 is a food science process and observation ontology dedicated to the eco-design of transformation processes and nutritional and sensory properties in the field of dairy products  [33, [35] [36] [37] [40] [41] [42] [43].
Multi-Criteria Reverse Engineering for Decision-Making in the Field of Food Packaging MRE could be used to mimic the decision-making process of the human brain and develop decision support systems (DSS) like EcoBioCAP software that help users take the right decision in the field of food packaging  [23]. In the subsequent texts, we borrow the EcoBioCAP tool as our main example to show how such tools have been developed at the junction between different fields of expertise, such as food engineering, computer science, knowledge engineering, argumentation, and numerical simulation. EcoBioCAP is a powerful MRE tool able to answer a complex multi-criteria query such as: BI want a packaging material that will maintain the quality of strawberries (i. e. with the permeability properties that match the respiration of strawberries), at a cost of less than €3 per kg, and if possible transparent and derived from renewable resources.
^Flexible querying methodologies employed in knowledge engineering were used to develop this tool  [48]. EcoBioCAP retrieves respiration characteristics from the product database and uses this data plus other user-entered characteristics, such as pack geometry, to compute the optimal permeabilities for the product. These permeabilities are automatically considered as mandatory preferences associated to selection criteria for the query, to which are added other mandatory or optional preferences that are determined by the user. The flexible querying module polls the packaging database to retrieve the material that best satisfies the query preferences and proposes as output a ranking of these materials. The DSS can manage both imprecise and missing data  [48 ]. An answer is guaranteed even if no material satisfies the mandatory criteria.
The Map'Opt application is used to adjust the packaging material to Bthe strict minimum^, i. e. , just those mass transfer properties necessary to maintain the protective atmosphere within a given range of values. This approach is an alternative to that currently used in industry for MAP of non-respiring products, which is based on the by-default use of high-barrier films to be sure of maintaining the protective atmosphere throughout the product's shelf life. Mathematical models for food engineering do allow some technical outputs to be computed but are not sufficient for decision making in an industrial world where choice of a packaging material is a multi-criteria decision. To take into account this aspect, the EcoBioCAP tool was developed to choose the most suitable packaging material for respiring produce from a dedicated database by answering bipolar multi-criteria querying (currently four criteria considered in the first prototype).
Practical Implementation and Description of the Tool The DSS was implemented as a web application accessible at http://pfl. grignon. inra. fr/EcoBioCapQuerying/. Short demo videos are available for download at http://umr-iate. cirad. fr/ axes-de-recherche/ingenierie-des-connaissances/themes-derecherche/ecobiocap-dss. The application interface is made of three parts as presented on Fig. 4:  -The upper part is dedicated to the permeability simulation and can be used to set the fresh food and packaging parameters. It plugs into the product database to retrieve the characteristics associated with the selected fresh food. Figure  4  (upper part) displays the optimal permeability properties for a soft cheese from raw ewe's milk, computed by the DSS, for a shelf life of 21 days in ambient temperature (20 °C), a food mass of 0.
These values correspond to the fuzzy preferences associated with permeabilities as presented on Fig. 5  and can be modified by the user before launching the querying of the packaging database. The use of fuzzy preferences allows to deliver a set of discriminated answers, which are ranked from most to least preferred. -The lower part is dedicated to the result of the query, as shown in Fig. 4 , again in the case of soft cheese. Note that, in this example, the process can rank the packaging with unknown values for mandatory criteria (the highest percentage of known values in the ranking is 60%).
In contrast, in the second case concerning food eco-packaging, the expected properties of the packaging are well-defined through criteria describing physical properties of the materials, which are themselves dependent on the gas exchange characteristics of the food product considered. In this case, the MRE can be re-expressed as a This highlights the linkage between MRE and data management issues. One way to implement MRE in the food sector is to take data management into account. Indeed, a DSS is nothing without its data, which are input parameters for the system to provide answers to the question asked. In order to design MRE systems, data management is required to parameterize numerical models. If numerical models are not available, we have to reason based on what data there is, by using database as well as descriptive data (e.
Here, the fuzzy preferences associated with CO 2 permeability, the fuzzy set BCO 2 perm^corresponding to the optimal permeances computed by the DSS and presented on Fig. 4 , are presented brine. ^A complete methodology has been proposed in three steps to apply mono-criterion RE to Bsalt uptake of the ripening cheese is low^: 1) Knowledge structuring in decision trees using mind map tools (like the Freeplane open source software for example, www. freeplane. org) based on interviews with domain experts (scientists, technical center engineers, cheesemakers, etc. ); 2) Knowledge translation into a knowledge representation formalism that enables automatic reasoning: the conceptual graph model has been chosen, as its graphical representation is easily understandable by non-specialists; 3) Reasoning on the decision trees encoded in conceptual graphs to find the actions associated with a given descriptor and, inversely, to find the descriptors impacted by a given action.
The most successful MRE-type application on meat and meat products was recently developed in the field of drycured meat products by Harkouss et al. [72]  and Mirade  [73]  who used Comsol® Multiphysics software to create a 3D multi-physical finite element-based model that predicts proteolysis (i. e. , the degradation of meat proteins that determines the final texture of the product), water activity, salt and water content distributions, and total weight loss during the different stages of dry-cured ham manufacture. This Bnumerical hamm odel can also be used to calculate mean values of all the predicted parameters in different groups of muscles previously identified in the ham geometry during its construction, and in the whole ham volume. This numerical model constitutes a multi-criteria numerical tool that can help industrial operators define ambitious technological scenarios, used in MRE to fit expected end-product properties, for the manufacture of low-Fig.