dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, in column 3, the sample RECENT_SWITCH considers only analysts who have switched brokers within the past year. We examine the likelihood of finding analysts working at two brokerage types. In Panel A, the dependent variable is 1 (and 0 otherwise) if the analyst is working at a Top-League Table  broker. League Table ranking measures the importance of each investment bank based on the dollar volume of security issuance (IPO, SEO, and public debt). We label a sell-side broker as a TOP_LT broker if it is associated with an investment bank that is ranked in the top 20th of the League Table (see Section C in the Supplementary Material for details about the data collection). In Panel B, we report results where the dependent variable is 1 (and 0 otherwise) if the analyst is working at an All-star-concentrated broker (ALLSTAR_BROKER).
e. , before recommendation changes are made public). We carefully adjust for after-tradinghour recommendation releases using their timestamps recorded in the IBES database. For instance, a recommendation change recorded after the market closes on Friday is pushed to the next trading day, and the strategy is to buy/sell the stock using the Monday's closing-day price. We also assume that if the recommendation is released in the last 15 minutes of the current trading day (after 3:45PM ET), it is pushed to the next trading day. This is because IBES recommendation timestamps are often delayed  (Bradley, Clarke, Lee, and Ornthanalai (2014) ), and such consideration helps make the strategy more implementable for ordinary investors. We create a daily portfolio that invests $1 in each upgraded stock and sells $1 in each downgraded stock.
Annualized We use the Capital IQ data set. Importantly for our purpose, and in contrast to other news data sets, the CIQ data set provides a very fine news classification. 12 We supplement the Capital IQ data set with earnings announcements and management forecasts from IBES and eliminate duplicate events found among these three databases. The merged Capital IQ-IBES database contains 98 distinct news items from 1. 14 million news. 13 To facilitate the interpretation, we aggregate these items into 14 broader topics. Appendix A. 2 shows the mapping of news items into the 14 news topics. About a third of the news corresponds to AGENDA COMMUNICATION which are the date of forthcoming corporate events (e. g. , investor day and annual meetings). These press releases typically inform the public about the date and the organization of the events which are unlikely to contain meaningful information; we remove them from our analysis.
We further remove topics that make up less than 1% of the data set, there are 9 news topics that we consider: EARNINGS; PRODUCT MARKET & OPERATION; MANAGEMENT FORECASTS; EXECUTIVE TURNOVER; M&A; PAY-OUT POLICY; SECURITY TRADING; SECURITIES ISSUANCE; and LEGAL ISSUES. We denote a recommendation change as being related to a specific news if it occurs within a [0; þ15] day-window after the news release. We choose the 15-day window because some news may take analysts longer to distill their contents as well as channel checking their sources. Our conclusions are qualitatively unaffected when using a shorter 1-week window or a longer 4-week window. We estimate the probability of observing recommendation changes made by slow-or fast-turnover analysts in relation to news flows. For slow-turnover analysts, we estimate the following logit model: Pr SLOW i = 1 ð Þ= α þ β 1 :EARNINGS i þβ 2 :MANAGEMENT_FORECASTS i þβ 3 :M&A i þ ⋯ þ ε i , (2) where the dependent variable in the logit function is equal to 1 if the recommendation change is made by a slow-turnover analyst, and 0 otherwise.
The sample consists of recommendation changes from 2003 to 2013. The dependent variable is equal to 1 when the recommendation change is from a slow-turnover analyst (or fastturnover analyst). All independent variables are indicator functions for various news type. Each independent variable is equal to 1 if its corresponding news was issued in the [À15, 0] calendar-day window before the recommendation change, and 0 otherwise. We consider 9 different types of news leading to a recommendation change. Earnings announcements and earnings guidance are from the IBES actual and guidance files, respectively. All other news events are from the Capital IQ "key development" data set. See Appendix A. 2 for classification of news. Industry and Year-fixed effects are included in the estimation. Robust standard error clustered at the firm level is reported in parenthesis below each estimate.
This could be performed in light of either one of the last years' crises, namely the COVID-19 pandemic or the Russo-Ukrainian War. The first beneficiaries of these studies would be the practitioners, who could adapt their strategies and policies as a result, making them suitable for unequally evolving industries. This paper can be viewed in light of some limitations. First, it only covers Scopus indexed papers on the topic of "career success". Other databases could be consulted in the future, although a significant difference in results is not anticipated. Second, the paper did not address an in-depth analysis of the 926 articles' content, which could be an interesting topic for future studies complementing the bibliometric analysis with qualitative details. Thus, compelling trends could be uncovered, while major world events are shaping career decisions.
, 2000; George et al. , 2001). Thus, studying the RC in universities and research centres provides insight into how they accomplish their third mission  (Compagnucci and Spigarelli, 2020). The SC of universities entails teaching improvement, internal collaboration, research management, and organisation management (Del Rocío Martínez-Torres, 2013). Bongiovanni et al. (2020)  defined the SC of universities as 'research outputs (publications and patents); knowledge creation processes and projects (seminars and research projects); impact and artifacts of scientific research (best practices, integrated research centres, guidelines and protocols, records, and databases); outputs of teaching (training); and educational outputs' (p. 485). Following the distinction tree presented by  Lövingsson et al.
Our study used the perceptual measurements of PhD holders from research centres and universities, who were part of the Minalogic cluster in Grenoble. A pre-test was performed on 32 researchers from research centres and universities in the Minalogic cluster in Grenoble. We used SPSS version 27 and AMOS to conduct the statistical analysis. We checked the reliability of our constructs with Cronbach's alpha; as our early statistics were satisfactory, the full survey was administered.
Data collection First, we created a database by gathering 1761 researchers from research centres and universities that were part of the Minalogic cluster. The data included the name of the person and research centre/university as well as the person's function, email, and phone number, when available. Second, we sent 1761 individualised emails mentioning the person's name and position, the research centre's/university's name, the reasons for choosing the institution in our sample, the project's cover letter, and the link to our online survey. Third, we sent two email reminders at one-week intervals. We obtained 248 responses: 141 from research centres and 107 from universities (a 14. 08% response rate) (Table  1 ). We used the organisation as our unit of analysis. Nine institutions were surveyed in the Minalogic cluster: the French Alternative Energies and Atomic Energy Commission (CEA), the French National Centre for Scientific Research (CNRS), European Synchrotron Radiation Facility (ESRF), Grenoble University, Joseph Fourier University (UJF), Pierre Mendès-France University (UPMF), Grenoble Institute of Technology (Grenoble INP), Polytech Grenoble, and Grenoble Ecole de Management (GEM).
Based on our data analysis, all factors were significant with a loading greater than 0. 5, which ensures a good convergence. We conducted pairwise correlations to check the discriminant validity  (Anderson and Gerbing, 1988)  (Table  2 ). Thereafter, we evaluated nomological validity by analysing two-by-two correlations and making assessments within the constructs. No items were excluded to avoid reducing the theoretical construct  (Hair et al. , 2006).
Normality and multicollinearity We performed skewness and kurtosis tests to assess the normal distribution of our data. Two items exceeding a skew index higher than 3 (equity transfer 3. 436 and cross equity 3. 203) were identified. Further, all the kurtosis indexes were below 10 (  Kline, 1998). We assessed the absence of multicollinearity using bivariate correlations and the variance inflation factor (VIF). In our empirical study, none of the bivariate correlations exceeded 0. 85, and all the VIFs were below 3. Based on the bivariate correlation and VIF, we assessed the absence of multicollinearity in our data collection.
Sample and data To test the proposed effects on users for whom the email was relevant, we used data from 10,701 customers who had previously given permission to be contacted and who owned the version of the app that needed updating (we also received data from 2863 users who did not need to update their app and analyzed data from these users as a robustness check). The users were assigned to one of the 2 Â 2 conditions. Given this random assignment, any systematic difference in user responses to the email and ensuing app usage must reflect user perceptions of the manipulated visual design elements and, more precisely, whether a GIF and/or emojis are present in (or absent from) the email body. To investigate overall email effectiveness, we also used data from 1396 users in a control group who did not receive the email.
We merged two rich datasets. First, for each user, the email provider data include multiple communication metrics to investigate behavioral reactions to experimental factors. The email data indicate whether a recipient clicked on either of the two update links (Click-through Rate) or asked to be taken off of the subscription list (Unsubscription Rate). Second, inapp data provided by the game app company includes daily downstream outcomes such as whether users updated the app (Update), how much time they spent in the app (In-app Time, measured through the sequence of activities in which users engaged within the app after receiving the email), and how much money they spent in the app (In-app Purchase). The in-app data were available at the individual level for 127 days before and three weeks after the email was sent.
The dependent variables encompass message outcomes (i. e. , Click-through and Unsubscription Rates) and downstream outcomes (i. e. , Update, In-App Time, and In-App Purchase). Data for all dependent variables were aggregated over a 3-week time frame after the email was sent (we test for various time frames in the robustness-check section and find similar results). To account for potential confounding factors, we measured several control variables: Recency (number of days between the last usage and the date of the email), Tenure (number of days between the first usage and the date of the email), In-app Time Before per Day (in-app time, aggregated for the 127 days before the date of the email and divided by the number of days on which a user was active), and In-app Purchase Before per Day (money spent, aggregated for the 127 days before the date of the email and divided by the number of days on which a user was active).
Acknowledgments We thank the anonymous company for the opportunity to run the field experiment and for insightful comments. We are grateful to  Jerry Grimes  for help with data collection for the eye-tracking study and acknowledge helpful comments from  Ivan Guitart  and the marketing department at  Grenoble École de Management. Finally, we thank the review team for suggestions on previous versions of this article. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This paper is part of the first author's dissertation.
Moreover, this methodology allows the collection of continuous and homogeneous data for a qualitative exploratory study. Reviewing an emerging field of study with literature coming from heterogeneous subject areas constitutes a methodological challenge. Hence, it is crucial to rely on a transparent and reproducible method to ensure methodological rigor. Similarly, Fosso  Wamba et al. (2013)  and  Pinkse et al. (2014)  used a rigorous methodological protocol by conducting an extended literature review of specific case studies and used their findings to present the current and future research directions in this area. To do so, they mobilized the relevant databases to identify peer-reviewed articles to obtain a classification framework of journal articles dealing with the selected case studies.
Data collection To conduct our data collection, we followed the literature review method based on journal articles that was adopted by Fosso Wamba et al. (  2013 ) and  Pinkse et al. (2014). In line with our exploratory research objectives and our longitudinal approach, this data collection method enables 'academics and practitioners alike  [to]  use journals most often for acquiring information and disseminating new findings and represent the highest level of research'  (Ngai and Wat, 2002, p. 416). Regarding the timeframe of our research, we focused on the acceleration of the commercialization of space activities and their entrepreneurial dynamics, which began in the 2010s (Dos Santos Paulino, 2020; Dos Santos Paulino and Gudmundsson, 2021) and is referred to as the 'New Space' era (Dos Santos Paulino and Le Hir, 2016).
We mainly selected articles from the 2010s to 2021, but we also included seven articles dating back to 1985, which were useful for historical data triangulation  (Yin, 2009). Then, regarding the selection of journals, we relied on our previous experience in space entrepreneurship research to select space-oriented and peer-reviewed journals 2 producing articles from 1985 to May 2021 with a managerial perspective: Acta Astronautica, New Space and Space Policy (see Figure  2 ). These journals provide empirical articles related to space entrepreneurial activities with a strong focus on the European and North American contexts, although they also deal with the non-Western managerial issues that the space EE has faced since the mid-2000s (i. e. , emergence of new space agencies and businesses in developing countries).
We chose not to limit our research to a specific geographical area because we aim to present a global approach to the space EE, which does not belong to one specific geographical area. We found 10,872 journal articles in these three journals in the Scopus database, which is the only platform where the three journals that we chose are registered (see Step 1 in Figure  2 ). We refined our results by limiting them to the 'Business, Management and Accounting' subject area (see Step 2 in Figure  2 ). We obtained 160 results, only from the journal New Space, and we screened these articles to identify seventeen keywords 3 that were used to characterize the space EE. We used New Space as a reference point because this journal focuses only on space entrepreneurial and innovation dynamics, which is not the case of the two other journals (i.
Funding: The authors performed this work with funding from the UrbaRiskLab (URL) project (https: //urbarisklab. org/fr/) and the RESIIST project (ANR-18-CE39-0018, https://research-gi. mines-albi. fr/display/resiist/RESIIST+Home (in French)) that is jointly funded by the French National Research Agency (ANR) and the General Secretary of Defense and National Security (SGDSN). The authors acknowledge these organizations for their support that helped improve the paper.
Lightweight ontologies are much used in industrial practice, e. g. , by all major internet search engine providers and tech companies, including Google, Amazon, eBay, Facebook, IBM, LinkedIn and Microsoft  [14]. They are relatively easy to use. Various tutorials and guidelines can be found to access such ontologies or provide data for them, e. g. , jsonld. com 9. However, their discussion in the academic community is rather sparse. Another flavor of ontologies uses modeling languages with much higher semantic expressiveness, such as OWL  10 or F-logic  [16]. Such ontology modeling languages allow knowledge engineers to express reasoning, logical operators, quantifiers, etc. Ontology examples are the BFO  11  and the Gene Ontology. Consider the following example from  [15]  formalizing the red color of strawberries using a set relation (subClassOf) and a quantifier (some):.
We notice that around those different ontology flavors, there are different communities dealing with them: rather developer-oriented communities for lightweight ontologies and academic communities for heavyweight ontologies. However, there seems to be minimal overlap and little communication between those communities. It seems like they live on different, disconnected islands. Why is this the case? Maybe there are fundamentally differing goals driving ontology engineering in those different communities. One goal for developing lightweight ontologies is developing knowledge-based applications like semantic search engines. And the software engineering principle KISS (keep it short and simple) also applies to engineering ontologies to be used in such applications. Consequently, modeling languages, guidelines and tutorials are as simple as possible.
Automated engineering of WordNets 12 A Wordnet is a lexical database of semantic relationships between words in a specific language. The first Wordnet  13 was created for the English language at Princeton University. As the usefulness of Wordnets as lexical resources became apparent, the Princeton Wordnet was expanded, and WordNets were constructed for other languages. OMW (Open Multilingual Wordnet)  14  is an open-source project that was launched with the goal to ease the use of Wordnets in multiple languages. OMW has the added benefit of connecting equivalent sets of synonyms (called "synsets" in Wordnet)  15  in different languages by means of an interlingual ID called "ILI". OdeNet  16  was constructed from open-source linguistic resources in combination with some manual and semi-automatic corrections.
Since OdeNet was constructed independently of existing resources in OMW, it was difficult to connect equivalent synsets in OMW via ILI. As an initial implementation, Google Translate 17  was used in combination with statistical methods as described by  Siegel & Bond (2021 [31] ). However, this implementation has some shortcomings, including: Incorrect ILI classification for some synsets from a semantic perspective. Duplicate assignment of some ILIs to multiple synsets. Part of speech (POS) for some ILIs being inconsistent between the English Wordnet EWN and OdeNet. A significant problem in using machine translation to connect equivalent synsets in different languages occurs when translating homographs (words with the same spelling but different meanings) and polysemes (words with the same spelling but different although related meanings).
As an example, various musical instruments were modeled with so-called Word sense definitions (WSD). A Google search for "wind keyboard instrument"  18  returns 24 million search results, most of which refer to pages with melodicas. Searching for pipe organ only returns the In addition to melodica and pipe organ, the word accordion is also found. The term accordion does not even appear on the first Google results page. Thus, DSS can be used to determine additional suitable search terms, which can then be entered in the respective search engine. Also, our sample query "wind keyboard instrument" was evaluated against WordNet Search  19. WordNet finds no result at all, only a search for "wind instrument" finds an entry that describes the term itself, but not the concepts that fall under it. On the basis of the Longman Defining Vocabulary  20  , we developed a Foundation Core Glossary (FCG) with 450 atomic concepts and approximately 2500 compound terms in eight languages.
Machine learning Machine learning (ML) is currently the most prominent AI approach, enabling models to make predictions based on previous observations  [27]. ML is used to power AI applications, but creating effective ML-based AI applications is complex. It requires high expertise, which experts like computer scientists or data scientists gather over the course of their careers. A great deal of gut feeling is involved. To less experienced scientists, this may look like a "secret art", making experts operate like magicians (we use the magician icon for illustration in Fig. 2 ). They apply their expertise and knowledge to analyze a dataset and create an efficient ML pipeline, which can be trained with a dataset to allow predictions for new data. Steps to be performed include data preparation, feature engineering, model selection, hyperparameter optimization, validation, and more.
g. , principal component analysis), 55 metrics (e. g. , accuracy), 16 ML libraries (e. g. , Scikit-learn), 21 AutoML solutions (e. g. , Autosklearn), their configuration items, interrelationships and more. The ML ontology is open source under GitHub  24. The ML ontology is the information backbone of OMA-ML and is used in various parts of the application: Firstly, in a configuration wizard, only plausible configuration options are shown to users. Secondly, the ontology guides the automated pre-processing of the dataset by determining if an automated pre-processing is required and which steps. Thirdly, the ontology determines the ML training strategy, guiding the metalayer with instructions on how to execute the training within the AutoML solutions. OMA-ML is actively being developed as an open-source project and can be accessed as a GitHub repository  25.
Finally, there is the user of the product. Users may be careful with a product or treat it badly. There may be accidents, lightning, repair, etc. in the lifetime of a product that may influence the way the product or its components can be recycled. This information is with the consumer or end user. But not only with end users, also with their agents and repair services. This is important information that needs to be collected. While a central database is easier to establish, the large variety of actors or sources of information and the heterogeneity of the information collected makes monolithic systems see their limits quickly. But what would such a distributed, product-centric system look like? The basic idea is to use GS1's Digital Link Specification  [20]  together with the Linked Data platform  [21]  and create a system of annotations to the physical product identified by some IRI.
Putting all the needed topics together, technical writers can, over the CCMS, automatically publish an operating manual, either as a PDF document or as an HTML snippet. To facilitate the modular authoring of legally compliant technical information within a CCMS, SOLIS-Doc provides the semantic model of an operating manual according to the European Directive as an RDF/RDFS ontology. The semantic model builds a minimally valid, and thus transferable, reference template of the entire mandatory content of such a legally compliant operating manual. The SOLIS-Doc Reference Template (see Fig. 8 ) can be configured and imported into a CCMS and provides technical writers with a reference structure including all the topics and fragments that they can use as a basis within their projects. Over the SOLIS-Doc reference template, all reference topics have already been pre-labeled with standardized context-related metadata of the new iiRDS 37 RDFS-based metadata model  [36].