dataesr/scientific-paragraphs-categorization huggingface dataset:
6  shows the RFS results for the above features. The group sample sizes are reported in Table  A. 1 in the Appendix. Plot (a) of Fig. 1  illustrates the histogram of Cohort Shapley values for each observation obtained from the dataset, therefore, the 'true' system. For companies with women leaders, the WCS value is -0. 025, represented by the orange vertical line. Based on Eq. (  6 ), the negative value of WCS indicates that women-led companies face, on average, disadvantages in their loan applications. This is because most of the cohort Shapley values 𝜙 𝐶𝑆 𝑡,𝑗 defined in Eq. (  5 ) are negative so being a women-led company has a negative impact on the chance of having the loan application approved. Coherently with the expectations and the results obtained in the literature  (Hewa-Wellalage et al.
e. , the advantage and disadvantage groups) is not obvious. Indeed, the computed RFS for women-led companies is 𝑅𝐹 𝑆 𝑊 𝑜𝑚𝑒𝑛 = -0. 025- (-0. 298)  |2×(-0. 298)| = 0. 46, and for men-led companies, 𝑅𝐹 𝑆 𝑀𝑒𝑛 = 0. 027-(-0. 300) |2×(-0. 300)| = 0. 54 , both of which are close to the RFS threshold of 0. 5 (See Fig. 6 ). Fig. 2  illustrates the results for the 'Ethnicity group'. Within the dataset, the Asian (red) and mixed-ethnic groups (green) are identified as disadvantaged, with respective WCS values of 𝑊 𝐶𝑆 𝐴𝑠𝑖𝑎𝑛 = -0. 173, and 𝑊 𝐶𝑆 𝑀𝑖𝑥𝑒𝑑 = -0. 142. Conversely, the white and black ethnic groups are observed to be privileged. In the biased system (Plot (b) of Fig. 2 ), notable changes in WCS values are observed for the Mixed and Asian groups. For company size, we consider the classification of the European Commission based on turnover and the number of employees.
Conclusion Based on our knowledge, this is the first paper that assesses the fairness of SMEs in accessing external finance. The main contribution of our analysis is that we consider not only accepted credit applications but also those that have been rejected. To achieve this goal, we analyze the data collected by the SME Finance Monitor survey, the largest survey on SME finance in the UK. To evaluate fairness, we consider the Cohort Shapley value because it bypasses the extrapolation issue in the Shapley value calculation, avoiding model evaluation on the infeasible combinations. We suggest the relative fairness score based on the Cohort Shapley value to measure the extent of privilege or disadvantage experienced by different groups based on factors such as gender, turnover, employment size, company age, and ethnicity.
47  Deference could not disguise every rebuke. To be required to reconcile anomalies between productivity and payroll records was a reminder to Tutwiler that daily returns were scrutinised, not just aggregate or quarterly returns. 48 To be asked to explain discrepancies in productivity records was to suggest that important data were not just incomplete or inaccurate but represented a failure to use data to make managerial decisions. Even more pointedly, the Managing Agent 'suggested' that to run on thin iron ore stocks, the plant was 'living hand to mouth'. Tutwiler's performance was assessed through continuous granular management accounting. Cost accounting meant that no decision was purely technical or exclusively Tutwiler's prerogative. All Tutwiler's decisions were centrally interrogated.
First, to stabilise the uneasy balance between the operational and strategic levels of TISCO and the Tata family. Second, to establish new accounting protocols that would mediate these diplomatic relations by providing shared reference points, accepted by all as legitimate measures of organisational performance and as proxies of managerial effectiveness. This created precedents and routines that gradually produced disciplinary power/knowledge. Solutions to practical problems were achieved without questioning American authority, but each administrative solution cumulatively built accountability and hierarchy. Padshah transformed intractable conflicts of principle into practical problems solvable through additional resources or amending data requirements. Each conflict was rendered productive of a solution in which all parties became invested.
Acknowledgments Our thanks to the Tata archivists,  Rajendra Prasad Narla  and  Swarup Sengupta  at  Pune and Jamshedpur. We are indebted to colleagues for their advice:  Amiya Kumar Bagchi ,  Charles Harvey ,  Michael Heller ,  David Higgins ,  Tom McGovern ,  Chinmay Tumbe  and  John Wilson.
Study population The Mont'Panier cross-sectional study was conducted from May 2018 to December 2019. Participants were recruited based on a call for participation, which involved reaching out via various channels such as local newspapers, radio programs, interviews on regional television news channels, as well as the distribution of posters and flyers. The target of this recruitment was volunteer households residing in the Montpellier Metropolitan Area (MMA), which comprises the city of Montpellier and 39 municipalities surrounding it. To be eligible for inclusion in the study, participants had to be 18 years of age or older and participate at least partly in grocery shopping. The survey involved two data collection tools: an online questionnaire and a food supply diary. The questionnaire gathered information on the household's socioeconomic and demographic characteristics, frequent activity locations and transportation methods, perception of the RFE and food purchasing behaviors.
The main respondent, answered these questions even if they concerned other household members. The household head was defined as the oldest active adult (or the oldest adult if there was no active member in the household), based on the definition used by the French institute of statistics (INSEE). The food supply diary was used to collect detailed information on food and beverage purchases for at home consumption made during one month, including location of purchase and details of the items purchased (e. g. price). Participants were encouraged to provide receipts whenever possible. Quota sampling was performed based on household composition (single adult, multiple adults, single adult with at least one child, and multiple adults with at least one child) crossed with the age group of the household head (<30, 30-50, and >50 years), using sociodemographic data of the MMA from the INSEE.
Grocery shopping practices Households' food purchases were evaluated over a one-month period using food supply diaries and grocery receipts. Only food purchases for at home consumption were considered for this study. To impute missing data regarding expenses for food items (N=454/58711; 0. 77%), we employed the INSEE database and food store websites. Mean prices per kilogram were multiplied by the quantity (in kilograms) when available. In cases where the quantity was missing, the quantity was initially imputed using the mean quantity of the respective food item within the study sample. The share of expenditures by FSS for each household was calculated by dividing the household's food expenditures at a specific food store by the total food expenditures across all food stores for that household.
Household characteristics The Mont'Panier study collected socioeconomic and demographic (SED) data through the online questionnaire. Household income per unit of consumption was categorized as follows: <980€, 980-1722€, 1723-2550€, and >2550€ per month (quartiles of income from the study population, the MMA). Missing data on income (73/665, 11%) were imputed through simple linear regression using the following variables: household composition, household head's age, employment status, and education level. Household composition was categorized as one adult, multiple adults, one adult with at least one child, and multiple adults with at least one child. Other variables included the household head's age group (<35, 35-50, >50 years), level of education (high school degree or lower, undergraduate degree, and postgraduate degree), employment status (employed versus not), car ownership (yes versus no), and home addresses.
Objective Retail Food Environment The location of FSSs was obtained through the Système d'Identification du Répertoire des Entreprises et de leurs Établissements (SIRENE) database of January 2019. SIRENE is a national business and establishment register database managed by the INSEE that records the identity of all active companies and their establishments in France. This database was first cross-checked using the database of OpenStreetMaps from April 2018, which provides open data of companies and establishments that can be updated and enriched by external contributors. Later, online searches on Google Maps, company websites of major food retailers, and city websites (e. g. for information about local markets) were performed to further verify the database. Final verifications were done through field observations of about 5% of the studied area: the information provided on the location and type of FSSs by the database were validated through ground-truthing in the city-region of Montpellier between May 2018 and January 2019  (20).
The classification of FSSs was based on the initial classification of food stores in the SIRENE database. This study focused on the following types of FSSs for the objective RFE: supermarkets, e-supermarkets (online food purchasing with pickup at supermarkets, known as "drive" in French), organic stores, markets (open-air and covered), convenience stores, bakeries, greengrocers, and other specialized food stores (butchers, fishmongers, and dairy stores). The objective RFE was assessed around participants' home and in their activity space. Addresses of their current home and main places of activity (i. e. work and/or other places they reported visiting at least once a week) were collected through the online questionnaire. Main places of activity of all household members who reported participating in grocery shopping, even if their involvement was minimal, were considered to determine the household's activity space.
Statistical Analysis Analyses were conducted on households with complete data for the variables used in the study, following imputation for missing food expenditure and income data. A weighted sample was used for all analyses presented in this study. Weights were calculated using the 'icarus' package of R Statistical Software (version 4. 1. 0) to ensure that the marginal distribution of the weighted sample was consistent with that of the targeted population. The sample was calibrated based on income per unit of consumption and household composition crossed with the age group of the household head, using data from the 2017 INSEE database. In order to identify patterns of grocery shopping practices, a two-step approach was employed, starting with a Principal Component Analysis (PCA) followed by a cluster analysis.
These analyses were conducted on the shares of expenditures by different FSSs. First, PCA was conducted to uncover underlying patterns and dimensions within the data (Additional file 1). The number of retained dimensions was chosen to obtain a cumulative percentage of variance of 95% (25). The inclusion of PCA was essential in capturing the nuanced relationships among variables and informing subsequent clustering. The results of the PCA were omitted from the manuscript to prevent unnecessary overload of information, they are however available in Additional file 1. A cluster analysis was then performed to identify distinct grocery shopping patterns based on the PCA results. Clustering was conducted using Ward's hierarchical classification of individuals, followed by K-means clustering, to maximize inter-class inertia.
Description of the study sample Out of the 738 participants enrolled in the Mont'Panier study, analyses were conducted on 415 households with complete data for the variables used in this study. Given the sample's adjustment by calibration on margins based on SED characteristics of the MMA population, distributions correspond to those of the general population. These results are presented in the first column of Table  1. The first column of Table  2  presents mean share of expenditures by FSS for the total sample. Half of households' food expenditures seem to have been made in supermarkets, a little more than one-tenth of expenditures were made in discounters and about one-fifth of expenditures were made in alternative FSSs, such as organic stores, markets and to a lesser extent producers. Ten percent of expenditures were made in convenience stores or online, with greater expenditures for convenience stores than for online stores.
This work was carried out as part of the first author's (DR) PhD funded by  Région Occitanie and Institut national de recherche pour  l'agriculture, l' alimentation et l' environnement (INRAE). The project " Sustainable Urban Food Systems -The effects of urban foodscape on food styles in Montpellier Metropole (Surfood-Foodscapes) " coordinated by  Cirad ,  INRAE , and  Montpellier Supagro , was publicly funded through  ANR  (the  French National Research Agency)  under the " Investissements d'avenir" programme  with the reference  ANR-10-LABX-001-01 Labex Agro  and coordinated by  Agropolis Fondation. The project  Mont ' Panier  " Relations entre paysages alimentaires et pratiques alimentaires " was also funded by  Région Occitanie, Dispositif REVE "REcherche et Valorisation Economique ". The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of this manuscript.
explicit and Tacit Knowledge Tacit and explicit knowledge are commonly differentiated in the context of supply chain and technology knowledge issues  (Evrard Samuel et al. , 2011; Schoenherr et al. , 2014; Suhindra et al. , 2020; Qiao et al. , 2021). While  Polanyi (1966 Polanyi ( /2009) )  first defined explicit and tacit knowledge in its human dimension,  Nonaka (1994)  and  Nonaka and Takeuchi (1995)  expanded this concept to organizational and interorganizational knowledge. Explicit knowledge refers to codified and documented knowledge embodied in written documents, procedures and databases of the organization. Information and communication systems such as ERP, the most common type of standardized information systems architectures  (Bidan et al. , 2012) , or collaborative platforms are the main drivers of explicit knowledge sharing in the supply chain.
SeCI Model of Knowledge Transformation -Socialization is the first step in knowledge conversion and consists of exchanging tacit knowledge and experience between individuals during formal and informal interactions. -Externalization is the second step of the cycle and occurs when individual tacit knowledge is converted in explicit knowledge shared by a group. The need to formalize and codify the tacit exchanges generates information exchange through documents, contracts and databases. -A third step, combination, then creates the organizational and interorganizational knowledge by communicating the explicit knowledge generated in the former phase at, for example, the organizational level or at the supply chain network level. In this stage common information tools are developed and used. -The last step consists of the appropriation (internalization) of the organizational or interorganizational knowledge and tools by individuals through a process of learning by doing.
Besides, we measured the supply chain information system maturity of the suppliers, 18% demonstrate a high level of information systems maturity, 58% a medium level and 24% a low level of information systems maturity. In addition, we collected internal documents such as student dissertations, Electra training and communication documents, and web site information. By using different data sources, and following the recommendations of  Yin (2018) , we have sought to improve the validity and reliability of the information collected. The data collection was sequential. In the first step, we determined the project's history, its scope and the key challenges. In the second step, we analyzed the survey database, seeking to identify links between portal use by suppliers, the characters of the suppliers and their specific relationships with Electra.
Moreover, in our literature review, we have referred to the common typology distinguishing tacit and explicit knowledge  (Nonaka, 1994) , which is particularly relevant in a supply Table  1. Main results of the supplier survey chain context  (Schoenherr et al. , 2014; Sudhindra et al. , 2020). The initial collaboration process shows a situation where only individual tacit knowledge highly related to persons is present. It is clear that the supply chain digital project enables the project team to create a real interorganizational knowledge shared by internal and external users and managers. The interorganizational vision is particularly significant in the context of global supply chains, since many partners will exchange information across geographic, linguistic and cultural boundaries. In the early 2000s,  Shore (2001)  noted that global supply chains raise major challenges for global information technology systems, suggesting appropriate data sharing strategies.
Using patent data, it is possible to track the quantity and quality of collaborations, both before and after the implementation of the Sunshine Act. 1  We construct a dataset considering the innovative outputs of 275 publicly traded medical device companies that were granted USPTO patents between 2005 and 2018, some of which were the result of collaborations with physicians. Physicians with a strong reputation due to their academic publications are designated as type-R inventors, while the remaining physicians are designated as type-N inventors. This allows us to use a difference-in-differences design to compare the effect of the Act on the two groups of physicians. Our findings show that while the Sunshine Act increased the number of collaborations by type-N inventors, it reduced the number of collaborations by type-R inventors, indicating there are winners and losers.
First, a "publicity" effect, which makes firms aware of all type-N inventors who have previously collaborated with any firm. The implication of this effect is twofold. First, since the firm has a wider range of type-N inventors to choose from, the expected quality of a project developed by the type-N inventor that has the best-fit increases. Whereas without disclosure the firm was selecting the type-N with a better fit out of the small subset of inventors that the firm had access to, with disclosure the firm can choose the type-N with a better fit out of all type-N inventors listed in the public database. Second, after working with a firm and being listed on the public database, type-N inventors no longer need to incur participation costs in the following periods if they want to collaborate with a firm.
| Effect on idea sellers Each of these channels (publicity and reputation) will influence the quantity of projects developed by inventors of type-R and type-N. First, the publicity channel will affect the incentives for type-N inventors to collaborate. Notably, this will occur right after the announcement of the disclosure policy (i. e. , even before information is actually made public). Indeed, in absence of the disclosure policy, a type-N inventor who wishes to collaborate with a firm must exert a participation cost (such as going to conferences) so that some firms are aware of him and eventually he may collaborate with one of these firms. However, after the collaboration is over, he is back to his initial position as an unknown type-N inventor. By contrast, when the disclosure policy is announced, there is an additional benefit to a type-N inventor from collaborating: after collaborating with a firm, the type-N inventor appears in a public database and may receive more offers to collaborate in future periods.
As expected, we found that the effect is significantly smaller for physicians in Massachusetts compared to those in other states (results available upon request). from firms to physicians. Payments subject to reporting are comprehensive, including "general payments" for consulting, gifts, trips and entertainment, meals, education materials, grants, and charity; current or prospective ownership or investment interest, royalties, and licenses; and research payments for different types of research activities, including any research collaboration between firms and physicians. The disclosed payment data are collected by the Center for Medicare and Medicaid Services (CMS). After inspecting and compiling the raw data, the CMS publishes a fully accessible dataset for each fiscal year. The first batch, including data from the second half of 2013, was completed and made public in 2014.
The disclosed dataset is constructed at the individual payment level. Each payment entry includes the amount of money, targeted medical product, information on collaborating physicians and firms, and payment purpose. Failure to report can trigger fines ranging from $1000 to $10,000 per unreported payment with an annual maximum of $150,000. For deliberate failure to report, the fine increases from $10,000 to $100,000 per payment with a maximum penalty of $1 million. Anecdotal evidence suggests that the improvement in the information environment determined by the Sunshine Act raised reputation concerns among physicians  (Sullivan, 2018a). Indeed, physicians generally "regarded the Physician Payment Sunshine Act as a personal and professional threat to privacy and reputation"  (Chimonas et al.
, 2017, p. 9) , whereas "critics of disclosure suggest that these laws stigmatize physicians who maintain collaborative relationships with industry"  (Chen et al. , 2019, p. 441 ). 5  Sullivan (2018c) documents two cases of physicians who disclosed that they are taking measures to avoid appearing on the database. Nogah Haramati, MD and professor of clinical radiology at Albert Einstein College of Medicine requires manufacturers "to provide a written guarantee that he receives no value and won't appear in the database for any activity or conference call he participates in. If the manufacturer can't provide a guarantee, he pulls out". Robert Hitchcock MD, argues that "most physicians […] will seriously rethink any collaboration they might have with drug companies". Moreover, he claims that "as a practicing physician, I really don't want my name on a list for having been to this CME [Continuing Medical Education conference] and gotten this amount of money from Pfizer and this amount from Schering-Plough.
(2014)  notice that the disclosure requirements may convey a distorted image of certain physician-industry relationships. In particular, "physicians conducting research involving donated drugs will have the monetary value of those drugs listed as 'research payments' within public databases". They provide an example under which an investigator enrolling just 10 patients would be reported as receiving $840,000 from a manufacturer for the drugs used in the treatment, even though "donated drugs are intended for use by patients and do not provide direct monetary value to physician-investigators. "  Morain et al. (2014)  conclude that "confusion over reporting for donated study drugs may have a chilling effect on physicians' willingness to participate in research, should physicians choose to avoid the appearance of financial relationships that raise the potential for misinterpretation.