dataesr/scientific-paragraphs-categorization huggingface dataset:
Financial dataset In this section, we consider time series of two stock indices, the Eurostoxx50 and the SP500. It is composed of n = 8265 observations of daily returns from 5 January 1987 to 27 March 2020. First, a preprocessing step is realized to get an ARMA-GARCH filtering of each of the marginal processes. The orders are selected by minimizing the BIC using the R package fGarch  [24]. For the Eurostoxx (resp. SP500) returns, an ARMA(0,0)-GARCH(1,1) (resp. ARMA(1,1)-GARCH(1,1)) model is selected. We denote by Xt,1 and X2,t the standardized residuals of the Eurostoxx and SP500 returns respectively, at time t. The vector of interest will be therefore Xt,I := (Xt,1, Xt,2). First, we study the past residuals Xt,J1 := LXt,I = (Xt-1,1, Xt-1,2) denoting by L the lag operator. Therefore, we can apply Algorithm 1.
Insurance dataset In  [8] , Frees, Lee and Yang have presented an extensive analysis of an insurance dataset from the Wisconsin Local Government Property Insurance Fund using multivariate frequency-severity regression models. Their training sample covers the time period from 2006 to 2010 and consists of 41 variables and 5677 observations. Each observation corresponds to a local government entity, which is a county, city, town, village, school and miscellaneous entity. Information on the type of a local entity, its number of claims and coverage sizes for a given year and insurance type, etc. , are recorded by these 41 variables. The training data from  [8]  consists of nominal (Type), categorical (Year), discrete and continuous variables. Continuous-type variables are non-negative and many of them have an atom at 0.
An application to an insurance dataset illustrates the proposed test methods at work. In most applications, conditioning events are not known. We construct them "blindly" and recursively in such a way to maximize the difference between conditional Kendall's taus, adapting CART algorithm to the dependence framework. The output is a binary tree representing the decision paths to explain dependencies given some conditioning events. The leaves of the tree correspond to the final partition of conditioning events that are considered. Applying this to a dataset of financial returns, the estimated tree turns out to be a binary search tree, reflecting increasing dependencies during crises periods compared to non-crises ones. The proposed framework and ideas can be adapted to alternative dependence measures, as Spearman's rho.
These maps make it possible to identify both future hotspots of deforestation and forest refuge areas (e. g. , concentrated in the heart of the Amazon, West Central Africa, and Papua New Guinea). An interactive map is available at https://forestatrisk. cirad. fr/maps. html. al. 2006 , Swann et al. 2015 , Aguiar et al. 2016). In this paper, 67 we model and forecast deforestation at the pantropical scale using 68 high-resolution spatial data. This was made possible by the recent 69 availability of pantropical spatial datasets of forest cover change 70  (Vancutsem et al. 2021 ) and of global spatial datasets of explanatory under a business-as-usual scenario of deforestation, 48% (39-56%) 88 of the world's tropical moist forest will have disappeared over the course of the 21 st century (Fig. 1  and Table  1 ).
Data for explanatory variables were extracted from extensive global datasets (World Database on Protected Areas, SRTM Digital Elevation Database, and OpenStreetMap) and had a resolution close to the original resolution of the forest cover change map (30 m, see SI Appendix, Table  S2 ). Sampling. For each study-area, we built a large dataset from a sample of forest cover change observations in the period 2010-2020. We performed a stratified balanced sampling between deforested and non-deforested pixels in the period 2010-2020. Pixels in each category were sampled randomly (SI sponding to areas of 144,163 ha and 142,647 ha, respectively (SI Appendix, 455 Table  S3 ). S18 ). We estimated the carbon emissions associated   S19 ). We used the IPCC   S20 ). We thus obtained three different predictions of the forest cover.
Purpose To systematically review studies focused on screening programs for renal cell carcinoma (RCC) and provide an exhaustive overview on their clinical impact, potential benefits, and harms. Methods A systematic review of the recent English-language literature was conducted according to the European Association of Urology guidelines and the PRISMA statement recommendations (PROSPERO ID: CRD42021283136) using the MEDLINE, Cochrane Central Register of Controlled Trials, and ClinicalTrials. gov databases. Risk-of-bias assessment was performed according to the QUality In Prognosis Studies (QUIPS) tool. Results Overall, nine studies and one clinical trials were included. Eight studies reported results from RCC screening programs involving a total of 159 136 patients and four studies reported screening cost-analysis.
Search strategy A systematic review of the English-language literature was performed according to the PRISMA criteria  [17]  using the Medline, Web of Science and Embase databases in November 2021. In addition, ClinicalTrials. gov was searched on December 15th, 2021 for potential clinical trials on RCC screening. The literature search used both free text and MeSH terms (keywords: "screening" OR "cancer screening" AND "renal cell carcinoma" OR "renal neoplasm" OR "renal cancer" OR "kidney cancer" OR "kidney neoplasm"). A detailed overview of the search strategy is available in the Appendix. The search strategy was adapted for the databases other than MEDLINE, as appropriate. An updated search was performed on December 15th, 2021 to identify additional relevant records. A manual search of bibliographies from included studies and previous systematic reviews was also performed.
Data collection and risk of bias assessment Data from the studies included in the review were extracted by two authors in a a-priori developed data extraction form. The reliability and completeness of data extraction was crosschecked by another member of the review team. When more than one article was based on the same study population, we included the most recent report. The same authors independently performed a formal risk of bias assessment using the QUality In Prognosis Studies (QUIPS) tool  [20]. A third reviewer acted as an arbitrator. The QUIPS tool provides a measure of the risk of bias over six domains of interest (Supplementary Table  1 ). The overall quality of evidence was assessed according to Grading of Recommendations Assessment, Development, and Evaluation (GRADE) recommendations (https:// www.
Current clinical trials on RCC screening Only one clinical trial on RCC screening is currently ongoing (Yorkshire Kidney Screening Trial [YKST); NCT05005195), sponsored by the University of Leeds in U. K. This is a non-randomized open label feasibility study for a RCC screening program in patients enrolled in the Yorkshire Lung Screening Trial that offers subjects at high risk for lung cancer aged 55-80 a CT scan as part of a lung health check. The study started on May 10th, 2021 and the estimated tudy completion date is February 28th, 2023. To perform a correct RCC screening, additional low-dose abdominal CT scan slices are performed to achieve a correct visualization of the kidney. Primary Outcome Measures include: the proportion of individuals who take up the offer of an abdominal CT scan; the acceptability of the combined lung and RCC screening approach to participants by noncontrast CT scanning; the acceptability of the combined lung and RCC screening approach to healthcare professionals; and the additional time required for the combined screening approach, including the time to provide information, consent participants, and perform the lengthier CT scan, the time needed by radiologists for reporting the CT scans, and the additional time to review abdominal CT findings.
Yet, combining RCC CT scanning with other CT scan based health check programs might be an option to increase the value and cost-effectiveness of such screening programs; this concept is indeed currently being explored in the Yorkshire Kidney Screening Trial (NCT05005195). There are a few studies exploring RCC screening in conjunction with other screening programs, such as abdominal aortic aneurism  [22]  or lung cancer  [39] , with the aim to decrease costs, invasiveness and to increase the diagnosis of a pathological finding, since these diseases have a low prevalence in the healthy population. In our review, the overall prevalence of renal masses/ RCC diagnosed by imaging ranged between 0. 02 and 0. 22% across the included studies (Table  1 ). It is important to highlight that while the incidence of RCC increases with age, screening elderly individuals with potentially higher competing risks of death might not be beneficial.
One clinical trial (NCT02923284) evaluated two biomarkers (urinary AQP1 and PLIN2) as a lowcost screening method to discriminate benign renal masses and patients with RCC from healthy controls  [14]. Further research is needed to validate these biomarkers as efficient and cost-effective screening tools at a population-level and in clinical practice. Our systematic review is not devoid of limitations. First, despite a rigorous review process and a well-defined PICO framework, our search strategy might have not retrieved all potentially relevant articles on RCC screening. Moreover, we limited our search to the English-language literature. Of note, we could not analyze the differential impact of available screening programs on the detection of indeterminate renal masses versus pathologically confirmed RCC, mainly due to limitations in the design of available studies.
2010 ; Griffin et al. 2010 Griffin et al. , 2011 ; ; Hou et al. 2011 ; Jacobs 2016 ; Jacobs and Müller 76,972 stocks, including 51,404  from developed and 25,568 from emerging markets. Naturally, the exact number and value of firms available per month are not constant over time and grow with the maturity of global financial markets (see Fig. A1  in the Online Appendix). Table  1  presents the basic descriptive statistics of the firms in our sample. To avoid survivorship bias, we follow  Lee (2011)  and  Hollstein (2020)  to include all listed and delisted stocks available in Datastream in our sample. In addition, we utilize Datastream Worldscope data types to prevent any problems associated with look-ahead bias and download data. Then we pull the data with five decimal places to mitigate possible return inaccuracies linked to foreign exchange conversions.
Data and methods for the US tests In this exercise, we closely follow Cosemans and Frehen's (2021) data sources, sample preparation, and methodological approach, particularly the analysis of historical trends in Table  9  (p. 477). Instead of Datastream, we obtain the price and return data from CRSP and Compustat. The sample contains all common stocks listed on the NYSE, AMEX, or NASDAQ, with a share price exceeding US$5. The study period runs from January 1931 to December 2015. We divide the full period into several subperiods of  Cosemans and Frehen (2021). Furthermore, as in our earlier tests, we split the equity universe into three size categories. Instead of the firm classification of micro, large, and small, representing 3,7, and 90% of the total market capitalization, respectively, we employ the original categorization of  Cosemans and Frehen (2021).
To explore the long-run US sample, as in Section 3. 4 , we use the CRSP and Compustat data and prepare it in accordance with  Cosemans and Frehen (2021). We use common stocks listed on the NYSE, AMEX, or NASDAQ, with a share price exceeding US$5. Furthermore, the 20th and 50th percentiles differentiate between micro-, small-, and large firms. The relevant asset pricing factors are sourced from  French (2020). The study period runs from July 1963 to April 2020. Meanwhile, the broad global sample encompasses all 48 international mar-   A17  of the Online Appendix. All the variables are lagged by one period in order to capture the announcement lag, reflecting the news associated with the indicator and its contemporaneous impact on the salience effect. The regression equations seen in the right sections include an additional six global factors from the model of  Fama and French (2018)  : market excess return (MKT), small minus big (SMB), high minus low (HML), robust minus weak (RMW), conservative minus aggressive (CMA), and winners minus losers (WML).
In contrast, the regressions in the left sections incorporate no control variables. The values in parentheses are  Newey-West (1987)  adjusted t -statistics. Panels A reports the results based on the CRSP sample that follows  Cosemans and Frehen (2021)  : it contains common stocks listed on NYSE, AMEX, or NASDAQ with the share price exceeding $5; furthermore, the microcaps are defined as the 20th percentile of market capitalization for NYSE stocks. The study period for the US data is July 1963 to April 2020, and the relevant asset pricing factor returns are obtained from  French (2021). Panels B presents the results for the broad global Datastream sample of stocks from 48 countries (excluding the UK The study period is from January 1990 to April 2020, and the microcaps are defined as the smallest firms, representing 3% of the total market capitalization.
Acknowledgements David Hirshleifer  and  Nikolai Roussanov  were the editors for this article. We are immensely thankful to the editors and the anonymous referee for insightful comments and suggestions. We also thank  Nicholas Barberis ,  Jonathan Batten ,  Si Cheng ,  Taufiq Choundry ,  Michael J. Cooper ,  Markus Dertwinkel-Kalt ,  Matthias Hanauer ,  Cambell R. Harvey ,  Fabian Hollstein ,  Martin Hronec ,  Kuan-Hui Lee ,  Hannes Mohrschladt ,  Koby Shemer ,  Ondrej Tobek , and  Baolian Wang  for helpful remarks, as well as the participants of the 34th EBES Conference in Athens, the 28th Annual Global Finance Conference, and the 37th  International Conference of the French Finance Association (AFFI). We are solely responsible for any remaining errors.
Further, economic experimenters now often use the same software to programme games on the computer, z-Tree. For the most common games, ready-made programmes are even available for download. 9 Several interviewees referred to it, and z-Tree is also used in the research centres studied by  Böhme (2016)  and  Sorgner (2017). Not just the physical settings of the labs, but the very computer interfaces on which the games are played are thus very similar across experiments. Coming out clearly from the interviews, then, and in line with previous research, is that the setups of economic experiments are quite standardized, in terms of both methodological rules and practical arrangements. Among the key elements of these setups, we find an apparently modest piece of text: The instructions.
8. Despite their moral connotation, the terms 'deception', 'lie' or 'honesty' should here be understood in a very technical way. Lying, or not lying, are two distinct techniques to induce behaviours in subjects, and ensure that these behaviours have epistemic value. 9. The software package is free, but publications from experiments using z-Tree must cite a 2007 article presenting the software. As of March 2021, this paper had been cited over 5400 times according to Crossref. https://citations. springer. com/item?doi=10. 1007/s10683-006-9159-4 10. Communication is not always completely banned, and can sometimes be the object of study, but it is always subjected to specific rules.
g. KPMG, 2020). On the other hand, this practice has neither achieved large-scale substantive adoption nor are there indications of widespread symbolic adoption of IR (see Figure  1 ). Both forms of adoption would result in a large number of integrated reports being published either in form of comprehensive reports or in form of broadly touted public commitments to IR that, however, lack evidence of actual implementation. Instead, despite its promising initial adoption rates, the further uptake of IR, in terms of the number of new adopters, has been stalling over an extended period of time (see the Corporate Register Database 1 ). Motivated by this empirical puzzle, our research question asks: why does the adoption of IR stall? We mobilize the practice adoption literature to answer our research question (e.
Search term: "Integrated Report" OR "Integrated Reporting" OR "Integrierte Berichterstattung" OR "Integrierter Bericht"; German pages without citations or patents; this search led to a broad variety of results, including German academic articles, practice-oriented pieces by academics and practitioners, integrated reports, and so on. Practitioner and Scholarly Literature in German (accurate as of  July 16, 2020)  according to the WISO database. Search term: "Integrated Report" OR "Integrated Reporting" OR "Integrierte Berichterstattung" OR "Integrierter Bericht. " This database covers articles in the German language in scholarly and practitioner-oriented journals published in Germany. Integrated and Combined Reports: Results from a manual analysis of all annual reports published by the 80 (in 2019: 90) largest listed German companies (covered by the two indices DAX and MDAX).
The IIRC defines IR as "process founded on integrated thinking that results in a periodic integrated report by an organization about value creation, preservation or erosion over time and related communications regarding aspects of value creation, preservation or erosion" (IIRC, 2021: 53). Thus, IR emphasizes the connectivity between economic, social, and environmental value creation. The final step of IR, which is visible and thus a manifestation of its formal and substantive adoption, is the publication of an integrated report  (Busco et al. , 2019). However, few companies publish an integrated report. Most firms are reluctant to commit to the new practice formally (see Corporate Register Database on https://www. corporateregister. com/frameworks/iirc/). This stalling of the adoption of IR is also observable in Germany, which serves as the setting of our research.
Discussing both past developments and the prospects of IR allowed us to expand the temporal range of our analysis. Overall, the data allowed us to construct a comprehensive case narrative  (Yin, 2018)  of IR in Germany. To enable empirical triangulation, we added secondary data for the period between 2008, when the first German integrated report was published, and 2019, when our research concluded. Specifically, we included practitioners' reports about IR, which had been published mostly by auditing and consulting firms in Germany that had been active participants in the field (data identifiers PR1-PR10). These publications contain normative guidance  (Palermo et al. , 2017)  that has the potential to shape the reporting practices of companies, as well as descriptive overviews and comments for practitioners.
Data analysis We coded the data in several consecutive steps, and we followed an inductive approach to analyze it (e. g. Corbin and Strauss, 2015; Gioia et al. , 2013). In the first-order analysis, we focused on our primary data from the interviews. We processed the raw data to identify an initial set of open codes, and we classified the descriptions of those codes into different groups of first-order concepts. The result of this analysis was our initial insight that there was a considerable degree of uncertainty about the benefits and pitfalls of IR and that the key actors in the field seemed to have put IR on hold. Using informant-centric data terms, we categorized our first-order analysis into two sets of descriptors that mirrored these insights. One describes the broader market environment of IR (see the left side of the data structure in Figure  2 ), and the other set describes how the organizations that are involved, including potential adopters as well as other stakeholders, responded to IR (see the right side of the data structure in Figure  2 ).
Even if these stakeholder groups occupied positions of prominence in the early days of the IIRC, the 2013 framework circled primarily on financial investors. Some informants thus perceived IR as being stuck between the two traditional reporting formats, both of which had a clearer focus on their respective target audiences. Moreover, despite the potential influence of large professional investors in the future dissemination of IR, our interviewees regarded the interest of this stakeholder group as extremely limited. The reason for this perception was that sustainability-conscious investors were expected to find the necessary information through existing reporting channels or in ready-to-use formats on specialized databases. Relying on established reporting formats, however, further circumscribed the supposed benefits of IR that supply-side actors promoted.
The IIRC indicated that the target group is the financial community. But they neither read sustainability reports, nor annual reports, nor integrated reports. They draw on their database providers that pick up this information. (#3-Acad) In summary, it was mainly auditing and specialized consulting companies and the IIRC as suppliers of IR expertise that pushed the initial proliferation of IR vigorously (see Appendix 3 for further illustrative quotes). Indeed, a majority of the interviewees who were working for actual and potential adopters bought-at least initially-into the positive arguments about IR that were being ventilated from the supply side of the market. These positive perceptions, however, changed significantly after the initial enthusiasm and demand for the adoption of IR waned.
Acknowledgements All authors contributed equally to this study. The sequence of authors was determined by a series of games of  Rock, Paper, Scissors, Lizard, Spock. They thank the editor  Amit Nigam  and three anonymous reviewers for their developmental feedback. The comments helped to improve the original draft considerably. They are also grateful to  Geert Braam ,  Stefan Heusinkveld ,  Bernard Leca , and  Andrew Sturdy  for their feedback on earlier drafts of this manuscript.
Breeze (2018)  used quantitative semantic analysis to probe the underlying ideology of corporate reports. Of all the different corpus techniques, the two which we will employ   Ibérica 43 (2022): 205-234 241  in this study concern the search for keywords and corpus concordance analysis. To do this, we use the Sketch Engine program  (kilgarriff et al. , 2014). keywords are words which are unusually frequent in a corpus compared with what might normally be expected and they can therefore be said to be characteristic of this corpus. Their importance is considerable, as  Culpeper and Demmen (2015)  have explained: Quantitatively defined keywords can indeed be of social, cultural, or political significance if they are characteristic of social, cultural, or political texts. That significance can be established by conducting qualitative analyses of the keywords identified through initial quantitative analysis.
It should also be noted that CaixaBank underwent important institutional changes in 2014 and a detailed explanation of these changes makes up a large part of the 2014 report. Once the reports had been acquired, we proceeded to read and analyse them. We created a general electronic corpus of all the reports in the Sketch Engine program and also two sub-corpora: one contained all the reports from 2013 to 2017 and the other the reports from 2018 and 2019. The reason for this separation was that we took the change in the name of the reports to imply changes to the orientation of their content and presentation. We then proceeded to look for keywords in each sub-corpus and we selected the top ten of these. To analyse the concepts contained in the reports more systematically, we identified the framing elements related to the keywords.