dataesr/scientific-paragraphs-categorization huggingface dataset:
Rule of Law WGI These data are from the World Governance Indicators Database of the World Bank. Rule of Law measures the extent to which agents have confidence in and abide by the rules of society, in particular the quality of contract enforcement, the police, and the courts, as well as the likelihood of crime and violence. Coded from -2. 5 to 2. 5 with higher values corresponding with better governance outcomes. Government Effectiveness WGI These data are from the World Governance Indicators Database of the World Bank. Government Effectiveness measures the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.
Coded from -2. 5 to 2. 5 with higher values corresponding with better governance outcomes. Corruption TI These data are from the Africa Development Indicators Database of the World Bank. This information is from the http://www. transparency. org Transparency International web site. More information may be available there. Corruption Perception Index score relates to perceptions of the degree of corruption as seen by business people and country analysts, and ranges between 10 (highly clean) and 0 (highly corrupt). Luminosity These data are from the 1km × 1km gridded data set constructed by  (Chen et al. , 2022).
A. 3 Data for the Cold War Interventions These data come from a variety of sources: government documents, such as declassified CIA documents and U. S. treaty agreements, expert reports, books and articles  (Porter, 1984; Schmidt, 2013; Shubin, 2007) , and online sources (http://countrystudies. us , http://www. countrydata. com, etc. ). Each intervention is categorized by which superpower intervened, the type of intervention and whether this intervention was hostile. The type of intervention is either military (arms deliveries or sales, military training, invasion, etc. ) or economic (e. g. , economic aid, non-military technical assistance and training , etc. ). Hostility is determined by whether the intervention was approved of by the recipient country. 1968. 197. 1972. 1974 Placebo Alliance Effect  Notes: This figure plots the root mean squared errors for the regressions of the probability of voting with the US on the 1000 placebo partitions.
Legal authorities, such as the Ministry of Health and Medical Education, must approve drug prices. A certain margin may be added to fees for each member of the chain. Every item is subjected to a monthly inflation rate, which affects the price of the item. Every drug needs a subset of raw materials for its manufacture, and the value of those raw materials equals 95% of the final drug's price when approved. To determine the demand for the simulated year, pharmacies and hospitals use data from recent years to calculate the anticipated demand. The input data used for determining the demand for two types of drugs was real-world data collected over the past five years by hospitals and pharmacies, upon which quarterly estimates were determined for 2022 and 2023. Seasonally, the winter season has a peak demand, while the following seasons gradually decline.
Simulation and Computational Results Simulation is incorporated in various areas of research  [41] [42] [43]. In this section, the simulation model is designed and constructed based on the intended flowchart. The pseudocode describing this process is presented in Algorithm 1. In this research, a three-step code is executed. As a first step, the initial data and database must be prepared so that they will be ready to be executed by the simulator engine. After this, the simulation model and the calculations associated with all the financial transactions and chain flow processes are performed through the simulation program for each member at each unit, and the system information will be updated. In the final stage, in the third and final step, the graphs and other information are received as outputs.
The proposed three-level supply chain model, stemming from Shafa Darou Investment Company and its subsidiary organizations, is simulated and analyzed with six pharmacies and three hospitals at the lowest level, two manufacturers and three distributors at the middle level, and one supplier for the two types of drugs at the highest level. With regard to the Shafa Darou Investment Company case study, two drugs, one of which is a high-consumption drug and the other of which is a low-consumption drug, are selected, and associated data is collected. Amoxicillin capsules and Pantoprazole tablets are chosen as high-demand and low-demand products, which are being produced in the holding, respectively. In this section, the simulation results are presented. The supply chain simulation model is coded in the Python v3.
8 environment, and the calculations are performed using a personal computer with 6 GB of RAM and an Intel ® Core™ i7 processor under the Linux v20. 04 operating system. The simulation of each day is performed in 0. 97 s on average. Communicating with the database in the coding environment was done using an ORM called SQLAlchemy (Version 1. 4. 32), while the two libraries Bokeh and PrettyTable were used to draw diagrams and record logs, respectively.
Data dissemination and collection In addition to benefiting from the feedback of the OADJS team, we also relied on their database. The OADJS survey has produced a population of 1,252 journals email addresses that agreed to be contacted anew by members of the team. This population has a significant advantage for our study: since we already had information about these journals (discipline, country, journal age, etc. ), we were able to be more specific. Our own survey was opened on June 16, 2021 via an email sent to the 1,252 journals. After two reminders, it was closed on July 12, 2021. 296 respondents opened and started to fill in the questionnaire. After extraction of the data on Excel spreadsheets, we proceeded to their cleaning: deletion of duplicates and uncompleted forms. In the end, we reach a total of 260 journals whose answers were usable for the analysis.
Quantitative analysis The majority of the questionnaire was structured by closed questions with predefined answers. Thus, many of the data was processed quantitatively and graphically visualized. For most of the quantitative treatments, we took the dataset for a question, built a table compiling the information, and produced a graphical representation. Since the number of answers from one question to another vary, especially concerning the list of publication acts, we systematically displayed rates rather than absolute values. We also performed some cross-referencing of data sets generated by the answers to several questions. Here again, we made use of the OADJS to put our results in perspective.
Acknowledgments This work was funded by the  French Ministry of Higher Education, Research and Innovation  through the  French Open Science Committee. We would like to thank  Odile Contat ,  Marin Dacos  and  Claire Leymonerie  as Ministry advisers for the study,  Victoria Brun ,  Morgan Meyer  and our colleagues at  CSI  for their insightful comments, our colleagues of the  French Open Science Committee  and our colleagues of the DIAMAS project who have made constructive remarks on earlier versions of this text.
We followed 20 drivers for their full shifts. These were day shifts (starting at 5 am), night shifts (until 2 am), or mixed shifts. We recorded notes in an observation notebook throughout our period of immersion. Observing the drivers' daily work routines gave us access to individual driver work tasks and to the time they spend with each other, including interactions with peers and superiors, as well as to the formal and informal rules that they have to comply with. The transcriptions given below were collected during these informal discussions and recorded in the observation notebook. Particular attention was given to discussions about changes in their professional environment and generational differences. The observation of people in naturally occurring settings allowed us to collect data without imposing external meaning on them  (Brewer, 2000).
We also collected our data from a "purposeful sample"  (Lincoln & Guba, 1985). We conducted 30 in-depth, semi-structured interviews with some of the drivers, as well as interviews with training and recruitment managers and with those higher up the hierarchical chain, to better situate the drivers in the sociomaterial environment in which they work. We did not try to identify a priori a "young" or "Millennial" category, as our interest lies in the social categorization of individuals. Table  2  summarizes the interviewee profiles. The interviews, which lasted from one and a half to two hours, were electronically recorded and fully transcribed. The interviewer did not raise the topic of generational issues to avoid influencing the discussion. The idea was to see if interviewees would themselves use generational discourse when asked about their general working experience and, if so, to analyze how they mobilize generational categories.
Methodologies In their comprehensive literature review,  Fethi and Pasiouras (2010)  focus on the role of OR and AI techniques in the assessment of bank efficiency and performance, covering the period between 1998 and early 2009. Other small-scale surveys and literature reviews can be found in  Paradi and Zhu (2013)  ,  Moro et al. (2015)  ,  Kaffash and Marra (2017) , and Gambella et al. (2021). In this paper, we cover the most recent decade, the period from 2010 to 2020, and review the increasingly important role of OR and AI methods in banking research. We followed a two-stage process for the identification of the relevant literature. In the first stage, the primary source is Scopus, which is the most comprehensive bibliographic database. More specifically, we searched research studies via different keywords, including both general and specific ones.
In the second stage, we screened the literature to focus on banking-related studies. This unique selection ensured that all the publications related directly to banking research, processing all publications obtained from the first step to identify those not directly related to banking research, including studies on central banks and firm-level lending. After excluding such studies, we identified 338 publications as the basis for our analysis (the complete database is available in the online supplementary appendix); Table  1  shows the number of different publication types by year. Fig. 1  shows the total number of publications on OR and AI in banking over the last decade. A noticeable increase in recent years, especially for 2019-2020, illustrates the increasing importance of methodological research in the banking industry.
Big data decreases the opacity between the banks and their customers, narrows the information asymmetry, and increases market stability. However, using highly efficient techniques to evaluate customers, such as applying sophisticated machine learning techniques to credit scoring, may lead to new biases among different groups  ( Fuster et al. , 2019 ) , causing potential risks in in-formation hiding. (iv) Role of regulations. With the development of fintech around the world, the World Bank Group has assembled its Global Database of Fintech Regulations, which includes country treatments of both foundational regulations, such as antimoney laundering, the countering of financial terrorism and the existence of rules to combat cybercrime, as well as regulations specific to fintech business models, such as digital banking and crypto assets and marketplace lending.
The aim of this article is to present to what extent the broad access to data by dominant platforms affects competition in the market and if this access reinforces their positions as incumbents. For this purpose, economic theories will be reviewed with emphasis on three ways in which access to data affects competition, namely: (i) price customization, (ii) recommendations and (iii) product and service customization. The article concludes with a discussion on antitrust analysis from the perspective of access to data concerning two merger cases that involved horizontal and vertical overlaps: the international acquisition of FitBit by Google (2020) and the national acquisition of Linx by Stone (2021).
As convergence occurs across all treatments, our experiments thus highlight that it is centralized market clearing, not centralized market information, that drives convergence. Our experimental design required running 104 individual market experiments, each with 10 or more trading rounds (involving 1. 7k+ subjects in total). The resulting data are welldocumented and freely accessible at the Open Science Framework (OSF) (under https://osf. io/gu62n/) and on GitHub (under https://github. com/ikicab/Trading-in-a-Black-Box). All markets are run with private information, of which over half with less than full access to orderbook information and to price histories. These low information treatments include "Black Box" treatments, where subjects do not receive any feedback from the order book about others' bids and asks or realized prices.
Importantly, it allows to quasi-constantly "refresh" an experimental subject's page, thus providing every user with real-time information about other users' bids and/or asks and realized prices (see Figure  A. 1 for an impression of the front end). In addition, the efficiency of the Web-Socket protocol allows running multiple trading rooms separately at the same time, each of which may be large. 51  To possess live control over the current status of the whole system (message dispatching and queuing, transaction count, database, query, etc. ) we developed a server monitor, which also contains an emergency module that enables communication with the participants in case of failure in the main server. The software is easy to handle, with a preprogrammed input mask for market components such as bids, valuations, market size, etc.
The central field is where own bids/asks can be placed. Notes: This is how our experiments were advertised on Amazon Mechanical Turk during the recruitment period. The experimental instructions contained a link to the trading Web page (see below) and a personalized access code. The flat rate participation fee was $1. 00 with a possible bonus as high as $7. 50 per 10 rounds of play, depending on treatment. The task overview read as follows: • Title: Take part in a market experiment where you are trading goods, WITH BONUS • Description: You will trade in a market and will receive your profits as a BONUS at the end of the game Notes: Subjects were able to play around with a noninteractive demo, where (depending on treatment) random bids and/or asks arrived on the two market sides. If the worker accepted the HIT and followed the instructions, he would next receive his personal access code to enter the trading experiment on the trading Web page.
Existing evidence suggest that innovative Small and Medium-sized Enterprises (or SMEs) are more likely to internationalise (i. e. have a greater propensity to export) than non-innovative SMEs. However, it is not yet clear whether and to what extent different types of innovation (i. e. product, service, and process) affect SME internationalisation. To address this issue, this study uses a research model that integrates the resource and institutional perspectives and empirically test it using data from the United Kingdom (UK) Longitudinal Small Business Survey. Our results confirm that SME internationalisation is more likely to occur in firms undertaking product innovation than process and/or service innovation, and a specific configuration of resource and institutional drivers influence SME internationalisation depending on the innovation type.
Data and sample This study uses the first wave of the Longitudinal Small Business Survey (LSBS). This is the most recent available survey dating back to 2003. This survey is compiled by the UK Department for Business Energy and Industrial Strategy (BEIS, 2016), and is one of the largest surveys comprising 15,502 telephone interviews with owners and managers of firms (<250 employees). Firm size, region, and industry sector were used to stratify the sample. Sources such as the Inter Departmental Business Register (IDBR) for registered businesses, and the Dun & Bradstreet's database for unregistered businesses were used in this sample. Technical details and survey instrument can be found in LSBS report  (BEIS, 2016). The survey provides a wide range of information on the key variables used in this study, including export and innovation activities.
It also covers the business profile details, including age, ownership, size, human capital, industry sector …etc. Moreover, it provides data on whether a firm exports product and/or services outside the UK; has the business introduced any new or significantly improved product, services, or process; resource drivers; and institutional drivers. Thus, this survey provides a rich data source that enables us to empirically examine the effects of different types of innovation (i. e. product, service, and process) as well as the effects of resource and institutional drivers on SME propensity to export. The final sample contains about 9188 SMEs (out of 15,502 contacted) and includes every sector of the economy, weighted to be representative of small businesses in the UK. Descriptive statistics of the main variables of interest are reported in Table  1  and Table 2.
We illustrate an application of the method to a proprietary dataset of search auctions held on a major search engine. The dataset consists of a large set of auctions for 71 different keywords: the search engine selected for us these auctions that involve popularly searched keywords, all having exactly 2 bidders acting under a common intermediary (i. e. , 2-bidder coalitions). 4  The application of the two-step method reveals that: (i) coordinated bidding is detected in 55% of the keywords analyzed, with most of the cases being classified as a relatively mild form of coordinated bidding. DGP argues that this form of coordination is indistinguishable from competitive bidding in a single auction, but our novel methodology is able to identify it by exploiting multiple observations from auctions on the same keyword; (ii) Despite its relative mildness, the effect of this form of coordinated bidding is not negligible, as the associated revenue losses may be as high as ranging between 5.
Data Our dataset is based on the internal records of one of the largest search engines. 5 This company keeps track of all the search auctions taking place on its dedicated platform. Thus, every time a user queries the search engine for a keyword on which at least one advertiser had placed a bid, the system creates a record which reports: the keyword, the bids of all advertisers winning a position as well as their identity, ad, quality score, rank, clicks received, and, if present, the identity of their agency placing the bid. The sample is based on a set of historical data from years 2010-2011, constructed as a representative sample of the search auctions involving some of the most frequently searched keywords. Within these "historical data," a subset was selected by identifying those keywords for which no more than one agency was active in the auctions and this agency represented exactly two advertisers.
This resulted in 71 keywords being selected. Then, the analysis sample was created by collecting all the search auctions involving these 71 keywords that were held during a randomly selected set of 12 days within a three-month time window around the end of 2010 and the beginning of 2011. 6 These keywords are from different industries and involve different sets of advertisers and intermediaries. Although they obviously cannot span the vast and diverse market of search advertising, they are a useful dataset to illustrate our method. Working with search engine data is a rare opportunity, but necessarily comes with limitations to the reporting freedom of researchers imposed by confidentiality agreements. 7 Hence, to help assessing external validity of the proprietary data, about which we are not allowed to disclose further information, we describe some stylized features of the market through publicly available data on Google sponsored search.
Thanks to the availability of these new data, this study presents a rare opportunity to analyze a phenomenon -bid coordination via intermediaries -that so far has only been possible to study in the theoretical literature. Specifically, we combine two datasets offering a snapshot of the Google search ads in the US market as of January 2017. The first dataset is Redbook, which links advertisers to intermediaries; the second is SEMrush, which links advertisers to search auctions. In particular, the Redbook data allow linking approximately 6,000 among the largest US advertisers to their marketing agencies and these agencies to their agency 5 The company name cannot be disclosed, due to a confidentiality agreement. 6 Specifically, one day was selected at random in each of the 12 sample weeks. 7 In fact, despite the stunning economic importance of the sponsored search auction, the confidential nature of the data has hindered their empirical analysis.
The evidence from the public dataset thus clarifies how certain features of the proprietary dataset are not the result of an arbitrary selection, but typical elements of the market. In particular, we refer to the use in the proprietary data of keywords with 2-bidder coalitions only and with no instances of competing coalitions. In panel C of Table  1 , we separate outcomes for the subsample of keywords with the 2-bidder coalitions. It shows that the keywords with 2-bidder coalitions represent well the full subsample of keywords with some intermediary bidding. In the following, Notes: statistics at the keyword level. The last four columns are for the full sample, while the first four are for the subset of keywords with at least one ad coming from an intermediary. Cost-per-click is in USD; Search Volume is the (average) monthly number of searches (in millions); the next three variables measure features of the keywords' length; Long Tail is an indicator variable for keywords composed by at least 4 words; Coalition is an indicator for the presence among the keyword ads of multiple advertisers affiliated with the same intermediary; Coalition size is the number of advertisers under the coalition, calculated exclusively for those keywords with coalitions.
It is particularly convenient because, as shown by  Varian (2007) , a simple rearrangement of the equilibrium characterization delivers testable predictions, based on the observables of the model and our dataset (namely, all variables except bidders' valuations). In particular, a bid profile is compatible with an EOS equilibrium (for some profile of valuations (v i ) i∈I ) if and only if for all j = 2,. , S: e j b j x j-1 -e j+1 b j+1 x j x j-1 -x j ≥ e j+1 b j+1 x j -e j+2 b j+2 x j+1 x j -x j+1. ( 2 ) Coordinated Bidding: Decarolis, Goldmanis and Penta (2020) (DGP) provided a theoretical analysis of the GSP auction when some of the advertisers' bids are placed by a common agency. The agency is modeled as a subset of bidders C ⊆ I, which places bids jointly for their members in order to maximize their joint surplus, subject to participation and stability constraints (note that this approach abstracts from the possibility of misaligned incentives between the intermediary and the bidders).