dataesr/scientific-paragraphs-categorization huggingface dataset:
Examples of RWE can include prospective observational cohort studies and retrospective case-control analyses of patient outcome data in existing datasets, among other noninterventional designs. Uncertainty analyses, such as one-way and/or multivariate probabilistic analyses, and multiway scenario analyses, help ensure the validity, reliability, and robustness of results from CEA  [16, 17]. CEA are often used to evaluate the value of influenza vaccines, compared with no vaccination or compared with other similar vaccine alternatives, and involve estimating an incremental cost-effectiveness ratio and comparing it with a willingness-to-pay threshold or opportunity cost in a fixed budget  [18]. Target audiences of CEA include health technology assessment (HTA) agencies, ministries of health, donor agencies, insurers, and private companies in some settings  [16].
The increasing role of RWE RWE is an important evidence source to input into CEA models based on disease factors (e. g. , RWE datasets can account for antigenic shift and seasonal variation), and practical factors, such as the need to evaluate patient-oriented, real-world endpoints that may not be assessed as part of RCTs (Table  1 )  [62] [63] [64] [65]. The WHO recognizes that observational VE studies have a larger role in policy for influenza vaccines than for other vaccines  [63]. The US Food and Drug Administration also provides several framework and guidance statements that support the increasing role of RWE in healthcare decision-making  [66]. RWE studies may produce more generalizable and clinically relevant results than RCTs, as RWE often emerges from diverse and large datasets  [64, 65].
Methods We designed a three-key steps to develop a list of candidate outcome measures to evaluate healthcare and applied these steps for the development of the international consortium of health outcome measures Heart valve disease dataset to illustrate the method. Our methodological approach involves: 1) Benchmark review of relevant registries and Clinical Practice Guidelines; 2) Applying machine learning to screen (using frequent words in abstracts) the studies that have been extracted from a systematic search of the literature using only the disease term; and 3) Extracting the candidate outcome measures from randomly selected batches of the retrieved studies iteratively until saturation is reached. Batch cutoff choices were investigated using data of 1000 simulated cases.
Introduction Value-based healthcare has emerged in recent years as the primary focus for authorities, decision-makers and the public  (1) (2) (3). Medical interventions are evaluated on the basis of their safety, e cacy and costeffectiveness  (4) (5) (6). Whilst randomized controlled trials (RCT) are the gold standard method for such an evaluation, the post-marketing surveillance of clinical outcomes using real-world data is increasingly demanded by regulators and the public  (1, (4) (5) (6). However, there is a need to standardize the methods by which these outcomes are de ned and captured. The International Consortium for Health Outcomes Measurement (ICHOM) has been founded to promote comprehensive standardized outcome measurement globally. In developing a standardized dataset, ICHOM aims to collaborate with international working groups, including clinicians, scientist, policy advisors and patients, in order to vote on candidate outcome measures and case-mix variables using an online modi ed-Delphi process  (7).
Methods The methodology comprises 3 complementary pillars: 1) Benchmark review of relevant registries and Clinical Practice Guidelines 2) Applying machine learning to screen (using frequent words in abstracts) the studies that have been extracted from a systematic search using only the disease as search term; and 3) Extracting the candidate outcome measures from batches of retrieved studies from another systematic search iteratively until saturation is reached (Figure  1 ). To show the feasibility of our approach, we illustrate this methodology in the ICHOM standard dataset for heart valve disease (HVD).
Pillars of standardized outcome measure development Pillar one: Benchmark review In most domains of disease, a wealth of registries and guidelines exist for the capture of clinical outcome measures. These resources provide excellent starting points for the identi cation of candidate outcome measures for a standardized dataset. In the case of the HVD dataset these resources included existing well-known valve registries, as well as international Clinical Practice Guidelines for the management of patients with HVD. Even in clinical areas where there is a lack of guidelines or registries (e. g. in rare diseases), efforts should focused on performing a literature search to identify consensus documents or relevant literature for this speci c disease. In the HVD case, national and international registries and guidelines were reviewed to extract the clinical outcome measures including patient reported outcomes measures (PROMs).
Pillar two: Text mining In order to obtain a quick overview of the outcome measures that should be included, text mining using machine learning can be utilized. That is, the use of a traditional scooping search (using only the disease as search term) at an initial stage and then apply machine learning to combine and analyze retrieved data. For instance, in the HVD dataset, the search term "Heart Valve Disease" was used in Embase yielding 142,279 articles (Table  1 ), with all the abstracts of the identi ed articles being separated in different text les. Using machine learning, there is an opportunity to create a Wordcloud from these text les  (9) , and visually present the frequency of the words that are used in the abstracts (the larger the word in the Wordcloud, the more frequently it appears in the abstracts).
The Wordcloud of the HVD search is presented in Figure  2  as an illustration. A quick scan of this Wordcloud suggests that outcomes such as mortality, valve regurgitation and stenosis are commonly discussed in literature. Note that the machine learning algorithm abbreviates words so that identical words with different su xes (e. g. valve and valves) are counted as one word. An example of R code to develop a Wordcloud is provided in Supplementary Text 3. Pillar Three: Iterative approach of reviewing literature A systematic literature review is an essential component in identifying candidate outcome measures for a standardized dataset. It is recommended to conduct a systematic search in collaboration with a medical information specialist. Multiple medical literature databases should be included.
As ICHOM focuses on outcomes of importance to patients, PROMs have been incorporated as an integral part of all ICHOM datasets. However, to ensure the capture of all relevant clinical outcomes (e. g. , mortality, complications), it is recommended to perform two separate systematic reviews focusing on clinical outcomes and PROMs. Duplicates articles should be removed before selecting relevant articles. In the HVD standardized dataset, we used a broad search to identify clinical outcomes and a separate systematic search for PROMs (Table  1 ). To balance the tradeoff between unnecessarily reviewing all studies and saturation of candidate outcomes (whether all relevant outcomes are identi ed) we propose an iterative algorithm of randomly selecting a batch of articles from an extensive literature search and keep reselecting new batches of articles until saturation of outcomes is achieved.
The PRISMA guidelines are commonly used to conduct systematic reviews of the literature. These guidelines mandate the review of all abstracts identi ed by the search, and recommend a full text examination of all the abstracts that meet the inclusion criteria (8). However, when developing outcome measures for standardized datasets, a huge number of studies may be identi ed given that the majority of observational and RCTs usually describe patient outcomes in these studies. As such, covering all these studies for a speci c clinical condition is an overwhelming task. Previous research suggested limiting the inclusion to the 100 most recent articles in the search, or restrict the search to only RCT and registries  (10, 11). However, both methods can introduce bias and miss out on important candidate outcome measures, due to temporal trends in outcome reporting or selective/limited reporting of outcomes in clinical trials/registries.
Conclusion In this paper we propose a novel integrated approach for the selection of candidate outcome measures form the literature. Such outcome measures provide a means for a standardized dataset to collect realworld outcome data to improve the understanding of the patterns disease across countries. The proposed approach is comprehensive, e cient and balances the tradeoff between extensively reviewing all literature versus potentially missing out on important outcomes. Simulation studies showed acceptable saturation of candidate outcomes using the iterative algorithm. We recommend that this method is adopted by the ICHOM initiative and other initiatives developing new standard outcome datasets.
Using the EBSCO Business Source Complete database, we then entered keywords as follows: "Decision Making" in the title, and "International Marketing" in all fields. This exercise returned 91 papers. However, not all were pertinent to organizational and/or international decision-making. Specifically, we found, articles on consumer, tourist and student (higher education) decision-making, and papers where the "international marketing" only appeared in the reference list, and therefore excluded these papers. This final cull yielded 42 remaining articles, which form the basis of this section.
e. , individual versus group decision-making), g) strategic versus operational decisionmaking, h) international issues (e. g. , cultural differences, country-of-origin effects), i) company size (i. e. , an SME focus), and finally j) supporting analytics through big data (e. g. , the effect of artificial intelligence on knowledge creation). Table  1  summarizes the 42 papers, the field they sit in, the themes they cover, the research methods used, and the theoretical perspectives underpinning the work. [Table  1  about here] The process of making decisions (DmP). The focus of this theme is not typically the types of decisions made in international marketing (see Decision context below), but the ways in which decisions are made. We see 12 publications on international marketing decision-making published between 2016 and 2021 devoted to this theme.
The empirical contexts from which the Special Issue findings are drawn also reflect the global relevance of the topic as those include data from Italy, China, and New Zeeland among others, thus offering a truly global perspective to current and future international marketing decision issues. In response to our initial call for papers, 36 manuscripts were submitted to the editorial office focusing on a broad range of topics. The manuscripts were initially screened and reviewed by the Special Issue co-editors in search of rigor and relevance to the theme. Manuscripts with significant potential were thereafter reviewed and evaluated by knowledgeable specialist scholars in their respective fields. After multiple rounds of reviews final decisions were made based on reviewer recommendations and careful reading by the Special Issue co-editors.
e. , low specificity and high convergence of rules). Guercini and Freeman's (this issue) findings enrich the literature on how international marketers make decisions through learning and heuristic rule usage whilst also focusing specifically on established international firms as opposed to new ventures where most of the extant heuristic research has been applied in the past. Li, Wang, Yang, and Tang (this issue)  further contribute to the decision-making principles stream but takes a rather different angle by investigating how the degree of owner-CEO narcissism influences decisions regarding the speed of post-entry internationalization among Small and Medium-sized (SME) exporters in China. The authors draw from the upper echelons literature and collect multi-wave survey data paired with objective indicators to find that more narcissistic CEOs tend to internationalize at a faster pace than their less narcissistic peers.
The authors use a multi study-approach consisting of actual customer review data combined with an online experiment. The experimental study further manipulates cultural thinking styles (analytic vs. holistic) to untangle the mechanisms behind the prior detected cross-cultural differences. Although Lee et al. 's (this issue) paper does not directly tackle the issue of managerial decision making, it does offer some valuable contributions to the international marketing decision-making literature in that it provides guidelines for managers in charge of global marketing platforms on how international customer decisions can be guided based on cultural backgrounds. The study's general findings can also be of use in international customer relationship management decisions, specifically when it comes to the techniques used for priming analytic versus holistic thinking prior to collecting international customer feedback, or when soliciting international customer recommendations.
Data and motivation The data are taken from a survey conducted by the Brookdale Institute in Jerusalem among immigrants from the FSU who declared, upon arriving in Israel, that they had trained and worked as an engineer in the FSU. This is the same survey used in BGL; however, while only the data for men were used in BGL, here we extend the analysis to include women. The interviews were conducted face-to-face and in Russian. A total of 1432 male and female immigrants were interviewed between June and December of 1995. The survey's most serious drawback is that it gathered little information about the immigrant's spouse, even if s/he was also an engineer. This, of course, makes it impossible to model the couple's joint decision making. The analysis is restricted to married engineers between the ages of 25 and 55 at the time of arrival, yielding a sample of 412 females and 614 males.
Several insights emerge from the data itself. First, the progress of the women in the labor market lagged considerably behind that of the men in terms of both employment opportunities and wages. Second, while a significant number of women are employed in part-time blue-collar jobs, there are virtually no men employed in part-time jobs whether blue-collar or white-collar. Furthermore, the employment patterns for both the men and women in the sample are parallel to those of highly educated native Israelis. We estimate two dynamic labor supply models that incorporates the features suggested in the literature, particularly the literature on the Israeli labor market. In the model estimated for women, women take the residential location of the family as given, we term this model the labor supply model (LSM).
This is because the datasets provided only limited information about the individual's spouse. Based on our assumption regarding the decision making of the two spouses, we run different models for men and women and arrive at different results for each. For example, in white-collar occupations there is much less variation in the regionspecific parameters among women than among men. Furthermore, the estimates reflect a large disparity in wages between men and women that ranges from 15 to 55% in all regions of the country and in both white-collar and blue-collar occupations. Finally, we find that for both men and women there is no return on experience brought over from the FSU, although there is a high return on experience acquired in Israel. Counterfactual simulations are used to decompose the wage disparity between men and women into its specific components.
The researchers conducted semi-structured interviews-which were transcribed, coded, and analyzed-with experts who were identified through secondary documents based on their expertise on the topic of inducement prizes and with regional policymakers, namely in less-developed European regions, to gather data on five regional challenge prizes. The examples of inducement prizes were identified in the Interreg Europe Policy Learning Platform database that has gathered more than 3000 good practices in regional development policies  [47]. The semi-structured expert interviews followed an interview guide that was adjusted to the specific role of each interviewee, namely whether they are regional practitioners or inducement prizes experts. The interview guide used questions in the OECD  [6]  (p. 38) report and aimed to explore the directionality, legitimacy, and responsibility of inducement prizes, the design and implementation of inducement prizes, and their policy transferability in LDRs.
The regional practitioners were asked questions regarding the directionality, legitimacy, and responsibility of inducement prizes and their design and implementation, while prize experts were also asked questions regarding their policy transferability in LDRs. The desk research consisted of building the interviewers' expert knowledge on regional inducement prizes and then triangulated with the semi-structured expert interviews. The desk research mainly came from three sources: academic articles on inducement prizes; reports on inducement prizes from consulting companies, innovation agencies, not-forprofit organizations, and international organizations; and regional practices from Interreg Europe good practice database. The data analysis consisted of triangulating the gathered information to "produce empirically-based findings" and were coded to understand whether inducement prizes have some TGIP characteristics, namely regarding directionality, legitimacy, and responsibility issues  [48]  (p.
, 2014). The deformations between surfaces are mathematically modeled as smooth and invertible functions (i. e. , diffeomorphisms). From a set of surfaces, an atlas of surfaces is created. The method estimates an average object configuration or mean shape from a collection of object sets (here the EDJ surfaces) and computes the deformations from the mean shape to each specimen. In addition, a set of initial control points located near its most variable parts, and a set of momenta parameterizing the deformations of the mean shape to each specimen are estimated  (Durrleman et al. , 2012). For each tooth position, the EDJ surfaces decimated to 50,000 polygons were manually oriented, then superimposed using the rigid and uniform scale option of the 'Align Surfaces' module in Avizo v. 7. 0 (FEI Visualization Sciences Group, Hillsboro).
This was done by minimizing the root mean square distance between the points of each specimen to corresponding points on the reference surface using an iterative closest-point algorithm. We used Deformetrica v. 4. 3 software  (Bône et al. , 2018)  to generate a global mean shape with a set of diffeomorphisms relating the global mean shape to each individual and the output (control points and deformation momenta) used to perform the statistical analyses to explore the EDJ shape variation and to classify the data. Deformations were computed at the IN2P3 Computing Center (Lyon, France). The output data were imported into R v. 4. 1. 3 (R Core Team, 2022) with the package 'RToolsForDeformetrica' v. 0. 1  (Dumoncel, 2021). Using the packages 'ade4' v. 1. 7-6  (Dray and Dufour, 2007) and 'Morpho' v.
2. 8 (Schlager, 2020)  for R, we first computed principal component analyses (PCAs), followed by between-group PCAs (bgPCAs) based on the deformation moments and using the following three groups assigned with equal prior probabilities: Pan, Gorilla, and Pongo. The Iberian Miocene specimens were then projected a posteriori into the bgPCA morphospace. We followed the recommendations of  Cardini and Polly (2020)  by computing cross-validated bgPCAs (cv-bgPCAs) to ensure that group discrimination is not spurious using the package 'Morpho. ' We also performed cross-validated canonical variates analyses (CVAs) using the same groups as for the bgPCAs using the R package 'Morpho. ' Since CVA computation requires the number of variables to be much smaller than the number of specimens, we computed the CVAs based on a subset of the first principal component (PC) scores showing the highest degree of correct classification (screening the correct classification results and selecting the minimum number of PC scores enabling to reach the optimum of correct classification;  Hastie et al.
Acknowledgments For access to comparative material, we sincerely thank  P. Bayle ,  A. Beaudet ,  J. Braga ,  E. Gilissen ,  M. Landreau ,  A. Mazurier , N. M emoire, and  R. Macchiarelli. We thank  Sergio Ll  acer for image processing, and  Sebasti a Calzada  for the loan of specimens housed at the MGSB. We gratefully acknowledge support from the  CNRS/ IN2P3 Computing Center (Lyon, France)  for providing computing and data-processing resources needed for this work. This research benefited from the scientific framework of the  University of Bordeaux 's  IdEx "Investments for the Future" program/GPR "Human Past ". This paper is part of  RþDþI  projects  PID2020-116908GB-I00 ,  PID2020-117118GB-I00 , and  PID2020-117289GB-I00 , funded by the  Spanish Agencia Estatal de Investigaci on  ( MCIN/AEI/10.
13039/501100011033 /), as well as a Ram on y Cajal grant to J. F. ( RYC2021-032857-I ) financed by the  Spanish Agencia Estatal de Investigaci on  ( MCIN/AEI/10. 13039/501100011033 )and the  European Union "NextGenerationEU"/PRTR. Research has also been funded by the  French CNRS, CERCA Programme/Generalitat de Catalunya , predoctoral fellowship ( PRE2018-083299 ;  Ag encia de Gesti o d'Ajuts Universitaris i de Recerca ,  Generalitat de Catalunya ) to Fl. B. , and the  Regione Friuli-Venezia Giulia (ICTP/Elettra EXACT  Project) in the frame of the  SAPIENS  Project funded by the  Centro Fermi. Fl. B. , J. F. and D. M. A. are members of consolidated research groups  2022 SGR 01188 ,  2022 SGR 01184  and  2022 SGR 00620 , respectively. Finally, we thank the co- Editor-in-Chief (Andrea Taylor ), the Associate Editor, and three anonymous reviewers for their comments that helped improve a previous version of this manuscript.
Groups and communities have been key topics in the information systems (IS) research agenda. While communities are assumed to emerge at the intersection of overlapping groups and their practices, prior research has mainly focused on their dynamics and evolution. This has resulted to limited empirical support regarding the emergence of communities. We address that lacuna by tracing the emergence of communities through the prism of resource mobilization theory. In doing so, we make use of a unique longitudinal dataset and incorporate Topic Modelling, Bipartite Network Analysis, and Community Detection. We show that new communities are formed at the intersection of overlapping groups and practices. In addition, we contribute to the IS literature by demonstrating that their emergence occurs due to resource mobilization that gives rise to a shared mindset.
To provide an answer to the aforementioned research question, we explore the emergence and evolution of communities through a longitudinal lens and focus on a community of consumption. Specifically, we explore the academic community around the topic of 'consumer identity' through a unique longitudinal dataset. In doing so, we use the academic outputs of consumer identity researchers (CIR) and incorporate novel computational approaches such as Topic Modelling (TM), Network Analysis (NA), and Community Detection (CD) on a corpus of academic outputs to trace resources across groups. Such an approach allowed us access to a wealth of data often missing in prior research on communities, while the novel context of our study contributes to better understandings, without negating the generalizability of insights for both research  (Struijk et al.