dataesr/scientific-paragraphs-categorization huggingface dataset:
Common method variance Because we collected data for both independent and dependent variables from a single source, we adopted two approaches to reduce common method variance. First, we guaranteed the anonymity of all the respondents to encourage them to complete the questionnaire honestly  (Gao et al. , 2021). Second, we used Harman's single-factor test, and we subjected all our measures to unrotated exploratory factor analysis using SPSS. 26. The results of the analysis confirmed several factors, while a single constrained factor accounted for only 36% of the variance, less than the 50% threshold  (Podsakoff and Organ, 1986). Overall, the results of these common method bias tests indicate that common method bias is not a problem in this study.
1 We highlight some of these deviations (see Table  4 ) in the belief they will help others select the right assumptions (and constraints) in future modeling-based research in this domain. Data for this study were collected through in-depth, on-site interviews with 39 facility managers making inventory decisions in private healthcare facilities located in low-income areas of Nairobi. The sample is academically interesting for three reasons. First, given the pace of urbanization in LMICs, it represents a context that is becoming increasingly relevant (Management Sciences for Health 2014, United Nations 2018). Second, the inventory control decisions taken by the facility managers in our sample are sub-optimal in a peculiar fashion: we observe low availability of many essential medicines and, at the same time, seemingly excessive quantities of other drugs.
Background In 2016, a large descriptive study of private healthcare facilities in Nairobi investigated medicine pricing  (Ongarora et al. 2019) , adherence to quality standards  (Abuga et al. 2019) , and availability of essential medicines. During this study, trained field agents did a physical inventory count at participating facilities to determine whether the items included in the Kenya Essential Medicine List (KEML) 2 were in stock. The field agents also used structured questionnaires to record details of all orders placed during an 8-week period (October to December 2016) to analyze the ordering process (i. e. , the rationale behind ordering decisions and selection of suppliers). Analysis of the physical stock count data found that facilities stocked only 28% of the items on the KEML.
We searched the PubMed and Web of Science databases for articles written in English within the last 20 years (going back from 1 October 2018), and available in full text. Search terms (see Appendix A) were chosen so as to find papers relating to (a) medicines, (b) over-/understocking, (c) healthcare facilities, and (d) low-income settings. Two authors independently assessed the abstracts of the resulting 150 articles. An article was included if its aim was to analyze availability of medicines in healthcare facilities in a low-or middle-income setting, and if this analysis was based on data (primary or secondary). We subsequently read the resulting 20 articles to assess (a) the study sample, (b) what the causes of over-and/ or understocking (if any) were said to be, and (c) whether data was presented in the paper to support these claims.
2008). The population from which the study sample was drawn consists of 381 private healthcare facilities in Nairobi County that were affiliated to PharmAccess To recruit facilities for the 2018 study, we approached the 45 facility managers in November 2018 and told them about the purpose, benefits, and risks of the study. Those who agreed to participate were asked to provide written consent to their data being used for research purposes, but they were allowed to withdraw from the study at any time. No financial incentives were offered to the facilities to participate, nor were there any negative consequences to facilities that chose to not participate. However, during the recruitment phase facilities were promised a post-analysis town hall meeting where results from our studies would be shared.
They were taped and later transcribed by an independent agency. The transcripts were analyzed using NVivo 12 text analysis software. Before we began the text analysis, we read through all the transcripts end-to-end, to familiarize ourselves with the full content of our interviews. In the first stage of the text analysis, parts of the interview that provided direct answers to the preplanned questions (see Appendix F) were identified and tagged to the appropriate question. For instance, question 14 asked, "Why is it important for you to have drugs on stock?," and was meant to explore the interviewee's motivations and incentives for stock-keeping and inventory management. Since our interviews were semi-structured, the conversation could touch upon motivations and incentives several times throughout the interview.
Our dataset does not allow us to assess the second aspect of inventory control skills, the capability to devise and adhere to policies and processes, since none of the facilities uses explicit inventory control policies. Time/human resource constraints. Time and manpower constraints could also lead to mismanagement of inventory and hence over-or understocking. As a facility increases in size it often becomes unfeasible for a single person to manage. Of the facilities in our sample, 61. 5% (24 out of 39) report that having more time would improve their inventory management. We believe that some of the facilities would benefit from hiring staff to take over mundane tasks, as this would free up the manager's time to deal with other issues. However, they report that it is hard to find trustworthy personnel.
EIO-LCA allows users to estimate total economic activity and emissions in a supply chain of a specific industry. EIO-LCA Purchaser models provided the data for the cradle-tofactory gate and all the transportation steps until the point of purchase. Therefore, using the Purchaser 2002 model of EIO-LCA (Carnegie Mellon University Green Design Institute, 2022), we calculated the normalized EIO impact category per dollar basis E cat (unit of emission/$) values for all the sectors studied in our study. Following EIO-LCA, TEA values per kg of material in each sector or kg of toxic compound which causes an economic activity in the healthcare sector were described as C sector ($/kg) in this article. C sector values were obtained from a combination of the MB database (M-Base, 2020), Brazilian Bank for Economic and Social Development Annual Report  (BNDES, 2014) , and literature review.
The MSWM costs for different scenarios included capital investment, equipment, and labor costs. Cost data and classifications of regional levels and SWM systems were obtained from BNDES (2014)' report about treatment technologies and final disposal of solid waste. In addition, CAPEX and OPEX values for recycling systems were gathered from  Larrain et al. (2021) 's work, which reflected the costs related to a fully developed mechanical recycling system. The monetary value of end products from these MSWM Assuming the best possible scenario of a mechanical R option, this system would provide maximum economical gain for the economy. However, there are technical limits for achieving a 100% circular economy with R. The higher thermal degradation during R processes of bioplastics and the lower final recyclate quality of PLA compared to PP increase the criteria to consider whether to go for R of bioplastics  (Briassoulis et al.
Procedures We conducted overall 24 experimental sessions at the Laboratory of Experimental Economics of Strasbourg (LEES). We conducted 8 sessions in June and October 2020 and 16 were conducted in February and March 2021. 11 In total, 384 subjects participated in the experiment. In each of the 4 treatment conditions: baseline, signing declaration treatment, neutral priming and sustainability priming treatment, 96 subjects participated in a between-subject design. Subjects were recruited from a list of experimental subjects of the LEES using the ORSEE software  (Greiner, 2015). The instructions were read aloud by the experimenter and before starting, a comprehension questionnaire was administered to check for subjects' understanding of the decision task. If a subject gave a wrong answer, the right answer was highlighted and explained to ensure that they fully understand the task.
Acknowledgements We would like to thank the TAMOBIOM research project team in particular  Francis de Morogues  and  Morgan Vuillermoz  for their contribution to the formulation of the research question and development of the experiment. We would like also to thank  Géraldine Bocqueho ,  Marielle Brunette  and  Benjamin Ouvrard  for their careful reading and their comments and  Kene Boun My  for experimental software programming.
Funding This research was supported by a grant by the  French Agency of Ecological Transition (ADEME , grant number:  1903C0001 ) and The  UMR BETA  is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE ).
UK & Greek banking market We now turn to our data characteristics. For the estimation of the model, we use data that consist of an unbalanced panel of all the financial institutions that provided credit during the years 1988-2011 in the UK and 1993-2011 in Greece. 13 Overall, both our samples account for a significant market share in terms of assets, loans, and deposits, occasionally even more than 90% in each respective category in both countries. 14 The number of banks we examine in our study changes during the sample period in both countries. This occurs specifically in Greece due to the many M&A that took place at the end of the 1990s (Rezitis, 2010;  Tziogkidis et al. , 2018). The observed wave of M&A events was triggered primarily by the willingness of the small banks to obtain a higher market share and secondarily by the privatization process initiated by the government, in line with the second Banking Directive.
18  Following the majority of empirical studies in banking (e. g. Fiordelisi et al. 2011; Philippas  et al. 2015; Casu et al. 2016; Baltas et al. 2017; Clark et al. 2018; Tziogkidis et al. 2018), we obtain our bank-level data from the Bankscope database of the Bureau Van Dijk company. We also obtain detailed information on M&A from the Zephyr database of the Bureau Van Dijk company. 19 All data are deflated using each country's GDP deflator (with 2011 as the base year) obtained from the World Bank database and converted to US dollars. We exclude Footnote 17 continued credit among others have been expanding at a rapid pace. As a result, the share of fee-based and other noninterest income to total income has increased dramatically. 17  Berger and Mester (1997) argue that not accounting for equity can result in a scale bias, while the efficiency of banks could be miscalculated even if they behave optimally given their risk preferences.
18 The selection of both the dependent and independent variables is consistent with several other studies in the literature, (e. g. Berger and Mester 1997; Lozano-Vivas and Pasiouras 2010). 19 We highlight crucial points of our data selection strategy that have been omitted by the bulk of empirical studies that have used Bankscope database (Claessens and Horen, 2012; Clerides et al. , 2015). This strategy is essential to ensuring the accuracy of results and inferences. First, both samples are checked for double-counted observations. Bankscope provides company account statements for banks and financial institutions worldwide, by collecting financial statements with both consolidated and unconsolidated statuses. Only the unconsolidated data are selected avoid double counting the same financial institution (in cases where unconsolidated data are not available, consolidated data were used).
With respect to the remaining 20%, where for some year observations the credit institution seems to change class, we highlight that this transition occurs in no more than two consecutive years and in the first year observations for all the credit institutions that belong to this 20% in both countries. The only rudimentary difference apparent in the results in both countries is that all the class membership determinants are statistically significant and larger than in the previous panel datasets. Consequently, we add to our previous findings that for both countries the credit institutions that belong to the first technological regime are more profitable compared with their peers in the second regime. Specifically, in terms of the Greek banking sector, it seems that the broader the variety of products the banks provide, the higher the probability for them to be classified in the first technological regime.
Footnote 19 continued merged in 2003 to create a new entity, bank C, then the two individual banks A and B are each included in the dataset until 2003. From 2003 onwards, these two banks' operations are considered to be terminated and the new bank (bank C) is included in the database. In the same spirit, assume that bank A was acquired by bank B in 2003; both banks are included in the database until 2003, with bank A then becoming inactive after 2003 and bank B remaining active after 2003.
It has found its widest applications in multicriteria decision making, planning and resource allocation and in conflict resolution. In (Online) Appendix 1, we present details about AHP and the algorithm used. The working algorithm follows that of Saaty  [38]  as detailed in (Online) Appendix 2. Two AHPs are used: the first (AHP1) prioritizes the uncertainty variables, followed by a second (AHP2) that is a layered AHP (with two layers of alternatives) that prioritizes the inter-dependence between variables. The normalized eigenvalues obtained from the AHP are taken as the weight factors. These eigenvalues have been rated from the data obtained from an Indian SME (anonymous) which is primarily a manufacturing SME in West Bengal, India. Usually, whatever method is used to derive these weights, they depend on the algorithm used and hence are subject to standard errors.
(  11 )). STEP 11: Derive the elements of the matrix using real data (Online Appendix 2). STEP 12: Identify the boundary conditions (Table  1 ). STEP 13: Solve constrained & unconstrained problems simultaneously. Independent verification is attained using MATLAB R2021a (bvp4c) and Mathematica 12 (structured Runge-Kutta 4) to solve the corresponding boundary value problems.
In this case, SP strategies may be represented by technological innovation or policy change. The environmental achievement is substantial and suggests that periodic threshold revisions will provide better results. Additionally, our model suggests that strong policy intervention will reduce carbon emissions. This will directly address SDG 13 and indirectly SDG  14 & 15. In summary, our model can identify the action points for implementation of SP strategies as well as the SDGs it targets to address. Obviously, the numbers and evolved variables are subjective of the datasets of the current SME, but the approach is generic enough for the technique to be ported to other challenging cases such as the waste management sector as well as biorefinery sectors.
Both are studied within the same Euler-Lagrange framework. Further optimization is carried out by solving the boundary value problems using MATLAB and Mathematica. The inputs and outputs of the model are all based on the 4 uncertainty pillars. Specifically with reference to the free energy F, the inputs are the variables Volume of CO 2 generated (VCO 2 ), Energy consumption due to processes involved (H P ), Water used due to the processes involved (W P ), Wastewater produced (W W ), No. of laborers (N1), No. of employees (N2), Number of social responsibility activities in a year (N3), No. of products sold (N4), No. of operations involved (N5), No. of type of taxes (N6), No. of shipments involved (N7) and No. of other logistics (N8), while the outputs are the variables Volume of CO 2 generation (VCO 2 ), Energy consumption due to processes involved (H P ), Wastewater produced (W W ) and No.
The two test cases can be separately solved using our own MATLAB codes within a few minutes, ensuring high computational efficiency of the algorithm proposed, leading towards a sustainable computing environment. The results clearly indicate that right strategy and the correct time of implementation play major role in business strategies, particularly in optimizing green production and profitability priorities. Our model is robust enough to handle diverse streamlined situations, e. g. , SME and beyond, a marked improvement on existing practices that are largely limited to objective decision making instead of constrained subjective inputs as our mathematical architecture entails. The findings address SDG 9, 11, 12, 13, 14 and 15. Future studies involving different data sets from waste management sectors are underway with more generic kernel structure contributed by machine learning techniques (neural network, SVM) with an aim to deliver a consummate decision-making toolbox.
Sample The empirical data used in our study was provided by The Annual Report of Social Information (RAIS) database maintained by the Brazilian Ministry of Economy. It contains comprehensive information about a large number of private firms. In order to assess the influence of women participation on the growth of SMEs operating in a context of crisis, we extracted data from 2015-2017, a period marked by a strong economic recession in Brazil  [29]. It caused the closure of hundreds of thousands of enterprises  [30] , a large decrease in the country's GDP and the rapid deterioration of labor market conditions  [33, 110]. In addition, it was in this interval that the number of women in the labor force started to decline steeply in the country, culminating in its lowest level in 2020  [111]. Such a milieu makes Brazil a meaningful context in which to test the impact of women's presence on SMEs affected by extensive economic downturns.
The RAIS database offered us information about 19,884 SMEs-with a headcount of between 10 and 250 employees-for the period from 2015-2017. We decided not to include microenterprises in our sample, i. e. , firms with less than 10 employees. The contextual and intrinsic differences between these firms and SMEs could lead to research biases  [112, 113]. In order to increase the homogeneity of our data, we refined our sample following four procedures: (a) we included only firms that were at least 10 years old in 2017 to ensure a similar level of business maturity. This is important because younger and older firms tend to present different growth paths  [114] ; (b) we excluded all firms that failed before the end of 2017; (c) to prevent gender polarization, we removed firms that had more than 90% of their workforce composed exclusively of men or women; and (d) finally, we removed all outliers, for all variables, by excluding observations that were three standard deviations away from the sample mean.
Measures The RAIS dataset provided us with information about the size, age, and growth of the sampled SMEs. Additionally, we also gathered detailed information corresponding to their employees, such as gender and wages, educational levels, and job tenure. We then used this information to compute our research variables. The dependent variable is SME growth, which indicates the ratio of new jobs added between 2015 and 2017. Given the variability of our sample in term of firm size (number of employees), we controlled the magnitude of SME growth to avoid analysis biases. This variable was adjusted considering the distance of each firm measure-in terms of standard deviations-in relation to the sample median (see Equation (  1 )). Equation  (1). Method of Adjustment (Firm Growth) The independent variable is women's participation in SMEs.
e. , a moderated moderation analysis-and women's participation in SME growth. This three-way interaction was examined using the Process macro (v. 3. 3) for SPSS Model 3  [120]. We followed the example of recent studies, e. g. ,  [121, 122]  and requested 5000 bootstrapped samples to ensure the significance and robustness of the conditional indirect effects  [123]. These effects are significant when the confidence interval excludes zero  [124, 125].
Model 1 The result of the stepwise regression (Table  3 ) shows that women's participation and its interaction with wage levels are significant predictors of SME growth. We found that We adopted Hayes'  [120]  procedure for the analysis of Models 2 to 4. This procedure is a conditional process analysis used to assess the complementary effects between the moderators-i. e. , a moderated moderation analysis-and women's participation in SME growth. This three-way interaction was examined using the Process macro (v. 3. 3) for SPSS Model 3  [120]. We followed the example of recent studies, e. g. ,  [121, 122]  and requested 5000 bootstrapped samples to ensure the significance and robustness of the conditional indirect effects  [123]. These effects are significant when the confidence interval excludes zero  [124, 125].
Note also that in both cases (359 or 190 data points), the rankings are identical. The results shown in Tables  3  and 4  are very similar. In both regressions, seven parameters are significantly different from 0 at the 1 percent level. The standard errors show that there is little, if any, difference in quality between the French wines and two Californian wines (Ridge Vineyards Monte Bello Cabernet Notes: The first three columns list the 1976 Paris rankings (1), names (2), and regions (3) of each wine. Column (4) the number of times each of them was observed in our data; Column (5) lists the number of times the rating of each individual wine was higher than the rating of the wine's region. Column (6) lists the corresponding ratios from the entries in Column (5) and Column (4). 2 ), and regions (3) of each wine.
One of the actions of this partnership took the form of a field survey of community leaders and households, undertaken by young community researchers using a smartphone application, KoboCollect  (KoboToolbox, 2019). In this data paper, we present the process of co-producing knowledge with the family farmer organizations at community and household levels and the two resulting databases. However, we do not consider that the "results" become available only a posteriori, i. e. , when the processes of data collection, analysis, and interpretation are completed. On the contrary, our research is "process oriented"  (Vandenbussche et al. , 2020). The entire process was aimed at empowering the actors to define the problems they wanted to investigate, with the perspective that this knowledge would become the basis for more inclusive action strategies.
4. 3). Before carrying out the data collection, we defined together the objectives of the survey: to understand the current state of family farming in the territory of the Santarem Plateau and the changes that have occurred since soy was implanted, including the strategies that were developed by the farmers to resist this expansion. A common framing was defined and shared, incorporating all the issues the stakeholders considered important. In this framing, three levels interact: the territorial level, where the main drivers (policies, governance, soybean expansion) can be observed, but also where social actors react through their organizations and networks; the community level, where the pressures (mainly the proximity to soybean) and opportunities (markets, local groups) influence the context in which households evolve; the household level, where the impacts on production systems and the main outcomes (economic revenue, wellbeing and health) can be observed.