dataesr/scientific-paragraphs-categorization huggingface dataset:
Wing Fung Chong was supported by start-up funds provided by the  Department of Mathematics and Department of Statistics, University of Illinois at Urbana-Champaign. Ying Hu  was partially supported by  Lebesgue Center of Mathematics  " Investissements d'avenir" program   ANR-11-LABX-0020-01 , by  ANR CAESARS  (Grant No. 15-CE05-0024 ) and by  ANR MFG  (Grant No. 16-CE40-0015-01 ). Gechun Liang  was partially supported by  Royal Society International Exchanges  (Grant No. 170137 ).
Thus, the material or economic reproduction processes of local societies, conceptually individualized, are only part of a larger process of total reproduction of the world system. The focus on the global scale and the long-term processes that characterize this approach provide a broader understanding of the systemic transformations, which in turn allows us to go beyond the Eurocentric bias of the neo-Schumpeterian scheme and to explore its implications in peripheral contexts. Finally, a word on the methodology. Our theoretical approach demands the use of multisite and long-term data. We draw on data gathered from 2012 to 2016. In-depth interviews, participant and non-participant observations and archival analysis were conducted in Ecuador, France and China. In-depth interviews were conducted with seven key informants who worked on the Yachay project and in SENESCYT (Ecuador), as well as several CEOs and project managers of innovation centers and start-ups in France and China.
As this process comes to an end, it makes more and more sense for us to set up a channel on YouTube, register our car on Uber, our house on Airbnb and ourselves on Tinder, than to look for a permanent job, join a union, save for retirement or meet people at the local bar. This new symbolic configuration is the code contained in the chip of "innovation, entrepreneurship and risk", which Andrade's inspiring talk said he wanted to implant in the minds of Yachay students. This phenomenon does not seem to be exclusive to a peripheral country like Ecuador. Data from complementary fieldwork carried out in France and China revealed that these kind of projects associated with innovation have played a central role in transmitting and reproducing the imaginary conveyed by the new techno-economic paradigm that has been unfolding since the 2007-2008 crisis.
Study design and participants In this prospective, cohort study, we analysed data from the Whitehall II study, a longitudinal cohort study of British civil servants. 18 The study began in 1985 in participants aged 35-55 years, with repeated data collection every 2-3 years. Participants had a clinical screening of behavioural and biomedical factors in 1985-88, 1991-94, 1997-99, 2002-04, 2007-09, 2012-13,  and 2015-16 and additionally completed a questionnaire on behavioural risk factors in 1989-90, 1995-96, 2001,  and 2006. The component measures of Fried's frailty phenotype were measured at the fifth, sixth, and seventh clinic visits. 1  The study was approved by the University College London Medical School Committee on the ethics of human research. All participants provided informed written consent.
Statistical analysis The study sample was defined as those who participated at the fifth (2007-09), sixth (2012-13), or seventh (2015-16) clinics and in whom frailty was assessed at least once. For each level of each risk factor, we calculated the number of individuals, total person-observations, and cases of frailty, together with the percentage of all observations that were considered cases of frailty. For each employment grade, we also calculated the percentage of observations that were cases of frailty from observations that fulfilled at least one of the five frailty conditions. Additionally, of the observations that were not cases of frailty, we calculated the percentage of observations that were cases of pre-frailty. We used multiple imputation for missing risk factor data, replacing missing values with values drawn randomly from the predictive distribution of each risk factor conditional on the observed data as specified by an imputation model.
This process created multiple imputed datasets that each had no missing values and accounted for the uncertainty due to the missing data. The imputation model contained all assessments of the participants' frailty status together with all the risk factors at age 50 years as described previously. 16 Additionally, the following auxiliary variables, which were associated with at least one of the risk factors with missing data, were included in the imputation model: data wave when participant was aged 50 years; values of all the risk factors at ages below and above age 50 years from adjacent data collections; waist circumference; prevalent angina, myocardial infarction, stroke, and systolic and diastolic blood pressure; family history of high blood pressure; and satisfaction with standard of living.
We generated ten imputed datasets that were analysed separately and combined the results using Rubin's rules. 24 e fitted logistic mixed models for frailty to estimate the associations between each risk factor at age 50 years and subsequent assessments of the frailty outcome. These models made use of data from all available clinic visits when frailty was measured while also accounting for differences in the timing of these occasions and the correlated nature of the data from repeated measures of   frailty from the same individual over time. For those attending the fifth clinic (2007-09), when frailty was first assessed, age was defined at the date of attendance. The timing of frailty measurements at the sixth and seventh clinics was accounted for by use of the interval since the fifth clinic as the time variable.
Acknowledgments We thank all participating civil service departments and their welfare, personnel, and establishment officers; the  British Occupational Health  and Safety Agency; the  British Council of Civil Service Unions ; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team. The Whitehall II Study team comprised research scientists, statisticians, study coordinators, nurses, data managers, administrative assistants, and data entry staff, who made the study possible. The Whitehall II study is supported by grants from the  British Heart Foundation  ( RG/13/2/30098 ,  RG/16/11/32334 ), and  British Medical Research Council  ( K013351 ).
This paper investigates how differences in military spending translate into probability of victory in a conflict. To do so the paper empirically estimates and compares the four main forms of contest success functions -t h e Tullock , the logit , the difference , and the relative difference form. In order to circumvent measurement issues and endogeneity biases associated with historical battle-related data, we advocate the use of virtual worlds. Data from virtual worlds is an innovative and promising tool in conflict research. It allows conflict analyses based on rich and objective empirical evidence on economic behavior in a warfare context; something which is difficult to achieve in real world settings. Thanks to collaboration with the developer of a virtual world, we are able to construct an original database of 19,229 battles that occurred during January 2011.
Virtual worlds are computer-created environments that visually mimic complex physical spaces, where people can interact with each other and with virtual objects in multiple ways ( Bainbridge, 2007 ). They give rise to sophisticated political systems governed by distinct sets of institutions and well-developed economies (e. g. Castronova, 2001; Castronova,  2008; Lastowka and Hunter, 2004; Lessig, 1999; Ludlow, 2001 or Mildenberger, 2013a ). Virtual worlds constitute a promising source of data as they provide a large amount of extremely precise data. Fine-grained analyses of battlefield behavior are possible as everything a user does within these worlds is registered on the servers. Thanks to a collaboration with the developer of a virtual world, we collected data on battles comprising more than 30 0,0 0 0 casualties in an online game called EVE Online (hereafter 'EVE').
4 EVE is a real-time massively multiplayer online game which closely fits a GVB framework. That is, EVE is neither an arcade game like Space Invaders nor a real-time strategy game like the Command and Conquer series, which both possess CSFs which are predetermined by the games' developers'. In EVE, you cannot let the computer fight for you -on "autopilot", so to speak -to let the game's "battle algorithm" automatically determine the result, but it is real people's software-mediated decisions and actions alone which decide the battle. The world of EVE is a virtual anarchic State of nature, in which the around 40 0,0 0 0 players of this game constantly face the paradigmatic choice of either making a living by producing things or by appropriating what others produced. We built an original database made up of 19,229 virtual battles between two 'teams' of players.
For the first period, the dataset comes from Bodart (1908) and consists of 315 battles. The second dataset is compiled by the Historical Evaluation and Research Organization ( HERO, 1983 ) and comprises 140 battles. Hwang (2012) uses a maximum-likelihood method to estimate CSF parameters and takes into account additional model selection criteria including the Akaike information criteria (AIC) and the Bayesian information criteria (BIC). According to his results, the Tullock form provides better predictions for 17th-century battles, but the logit form has a better goodness-of-fit for WWII battles. According to the author, the key explanation for this shift is the evolution of military technology: "The range of rocket missiles became longer and the advantage of electronics in the form of radar and improved radio communication was tremendous.
Jia et al. (2013) also highlight a reverse causality issue in historical battlefield data. Indeed, battles are not independent one to another: a previous big victory (resp. defeat) may impact the troops' morale, and therefore the outcome of the next battle. In such conditions, it appears hard to establish a clear relation between fighting effort and probability of winning. But any attempt to empirically estimate CSFs has to address these difficulties. Jia et al. (2013 , p. 220) hold that "it would require an unrealistic amount of data to achieve any reasonable power in testing between them [i. e. different forms of CSFs] statistically" (emphasis is ours). Given the issues just oulined, one may add that it also takes an unusually precise dataset. That is exactly why we promote the use of data coming from virtual worlds.
Advantages of virtual-worlds research Virtual-worlds research combines the control of laboratory settings and the ecological validity of fieldwork ( Fiedler and  Haruvy, 2009 ). The rich data available allows us to zoom in on the phenomenon of interest, controlling for spurious factors, thus raising construct validity to levels usually seen only in experiments. For example, EVE's servers hold data concerning all the fights a user engaged in, the amount of material resources he invested, the size of his losses, the extent of damage he dealt to the opposing force, and so on. Consequently, we are able to measure the fighting effort devoted in a battle by each participant in great detail. Ecological validity is high, as users do not fulfill given tasks but act in a natural habitat. This allows us to circumvent, for instance, effects typically seen in lab experiments like increased cooperation-levels in repeated experiments ( Holmes, 1967 ).
16 o conclude, as regards the relation of fighting effort s to battle outcome, it is closer to the truth to state that nothing but the environment's physics and technology are determined by the developers. All of the aspects mentioned make EVE a particularly realistic game in which battle outcomes are unpredictable in the sense that human choice rather than a predetermined CSF is decisive. On a more general note, consider that whenever we empirically measure a CSF based on some particular dataset, we are measuring "merely" the CSF of, say, basketball or of World War II. There is no context-free CSF. And concrete estimations always measure the CSF of something.
Data In order to estimate and compare the four forms of CSFs, we built an original database based on battles in EVE in January 2011. As the EVE server data we rely on contains no information about battles -like when a battle starts and ends, who is fighting whom together with whom, etc. -but exclusively lists data concerning individual kills (see Appendix A ), we rely on an R script to identify independent battles in the pool of over 30 0,0 0 0 kills that took place in nullsec space, and which did not involve any non-player-controlled (NPC) characters or intra-group kills. Starting with the first kill on January 1, at 0 0:0 0 a. m. , the script individually tests for each kill whether other kills happened in the same solar system no more than 30 minutes later. 17 If at least 2 kills happened within a given solar system within 30 minutes, we start out considering these kills to jointly constitute a battle.
It does so chronologically, i. e. it allows for one player to bring a first ship to battle, to lose this ship, to come back with a second ship, and so on. These numbers are added up to give team values. Our script initially identified 19,449 battles. However, we dropped 220 battles of our sample due to missing informations relative to important characteristics for our studies (missing values in ship investments, or in skills of players involved in a specific battle). Yet the data loss rate of 1. 13% is low, and our final dataset still consists of 19,229 battles. Our outcome variable, W , is a dummy equal to 1 if Team 1 wins the battle and 0 in case of defeat. As described above, EVE does not possess a developer-determined CSF of its own which is integrated in the game code and tells us who has won a battle.
Fighting effort s are directly observable in our dataset in the sense that we have the precise value (in ISK) of all ships brought to the battlefield by each team. To be consistent with previous notations, the total value of ships is labeled G i , i = 1 , 2. We see in Table  1  that G 2 is much bigger than G 1. This is again related to how our script operates. It is reasonable to expect that Team 1 is more prone to be outnumbered. The military power of an army does not only depend on the value of its weapons, but also on the abilities of the soldiers and on how experienced they are. Our dataset allows for refinements in that direction, as the fighting skills of each individual player involved in the battle are available. These fighting skills are scaled from 0 to 9, 0 being a very new player, and 9 a very experienced one.
4 ). In our third robustness check, we truncated our database by eliminating the first and the last 2. 5% of kills. One can easily see in Table  A. 8 that results are quite stable regarding our baseline estimations. Fourth, we checked for the importance of dynamic effects. We find that neither the average number of prior battles per team, nor the average historical win ratio per team affect the robustness of our results. Put differently, our baseline coeffi-  23  We checked for multicollinearity issues regarding the link between our measurements of difference in fighting efforts x k and DASP. First, correlation coefficients are very low. Second, removing DASP from baseline regressions involves a negligible impact both on the value of m and on standards errors associated. 24 Otherwise, in small battles the last player killed would systematically determine the outcome of the battle, which is not realistic.
050) (0. 047) (0. 024) Constant -2. 382  *  *  *  -2. 9  *  *  *  -1. 715  *  *  *  (0. 045) (0. 043) (0. 019) Obs. 19 229 19 229 19 229 LL -3 802. 69 -4 398. 03 -4 494. 11 AIC 7 611. 36 8 800. 06 8 992. 22 BIC 7 634. 95 8 815. 79 9 007. 95 Table A. 7 Alternative measurement for victory. Tullock Logit Probit m 0. 933  *  *  *  1. 53e -10  *  *  *  7. 72e -11  *  *  *  (0. 304) (5. 54e -11 ) (2. 67e -11 ) DASP 3. 988  *  *  *  4. 254  *  *  *  2. 274  *  *  *  (0. 679) (0. 674) (0. 308) Constant -3. 39  *  *  *  -3. 662  *  *  *  -1. 932  *  *  *  (0. 598) (0. 592) (0. 246) Obs. 198 198 198 LL -48. 98 -49. 10 -49. 47 AIC 103. 97 102. 20 102. 93 BIC 113. 83 108. 78 109. 51 Table A. 8 Truncating database. Tullock Logit Probit m 0. 805  *  *  *  5. 65e -10  *  *  *  8. 08e -11  *  *  *  (0. 025) (3.
In addition, although prognostic studies show greater risk of adverse outcomes in those with multimorbidity, the factors that modify this risk remain unknown. A further limitation of previous research is the piecemeal approach, where studies examine the risk factors for multimorbidity and the prognosis of multimorbidity in separate analyses, often with cross-sectional data used to examine the former. Thus, the characteristics that adversely affect temporal progression from a first chronic disease to multimorbidity and subsequent mortality are unknown. The identification of such factors would pave the way for interventions that target the specific factors associated with the incidence and prognosis of multimorbidity. In order to address some of these limitations, we examined the role of clinical, socioeconomic, and behavioural factors at age 50 years in the transitions from a healthy state to first cardiometabolic disease, cardiometabolic multimorbidity, and subsequent mortality using data spanning 30 years.
Assessment of risk factors We extracted data on risk factors at the clinical examination closest to age 50 years, allowing a ±5-year margin, to remove the effect of baseline age in our estimates. This was done by using data from the first 4 waves of the study  (in 1985, 1991, 1997, and 2002)  when the age range of participants was 35-55, 40-64, 45-69, and 50-74 years. Thus, follow-up started at age 50 for all participants, and analyses were restricted to those free of cardiometabolic disease (diabetes, CHD, stroke) at age 50. Three sets of risk factors were examined, each on a 5-point scale, with 0 denoting the lowest risk, and 4 the highest risk. Clinical profile was assessed using 4 measures, with thresholds defined using World Health Organization guidelines. Hypertension was defined as systolic blood pressure ! 140 mm Hg, diastolic blood pressure ! 90 mm Hg, or use of antihypertensive medication.
Ascertainment of cardiometabolic diseases and multimorbidity Incidence of cardiometabolic multimorbidity  [4] , defined as at least 2 of CHD, stroke, and diabetes, was based on data from the clinical assessments between 1985 and 2016 and linkage (until 31 August 2017) to the national Hospital Episode Statistics (HES) database with in-and outpatient data, undertaken using the National Health Service (NHS) identification number. In the UK, the NHS provides most of the healthcare; private medical insurance is held by around 12% of the population (1997 figures)  [24]  and is used mainly for elective surgery rather than chronic conditions. The HES database contains the exact date of diagnosis (hospitalisation/outpatient visit), allowing greater precision in the time of follow-up. Whitehall II ascertained non-fatal CHD based on 12-lead resting ECG recordings, coded using the Minnesota system, and on self-reported CHD that was corroborated with information from the general practitioner or manual retrieval of hospital records.
Results The analyses were based on 8,270 of the 10,308 participants recruited to the study in 1985 (flowchart in S1 Fig), followed over a mean 23. 7 (SD = 5. 9) years. As all risk factors were drawn at around age 50 years, participants older than this at baseline, those who dropped out of the study before reaching this age, and those with missing data on risk factors at age 50 were excluded from the analysis (N = 1,581). Table  1  presents the sample characteristics at age 50 as a function of cardiometabolic multimorbidity and mortality status at the end of follow-up; these characteristics in relation to the 3 individual diseases considered in the analyses (diabetes, CHD, and stroke) are shown in S1 Table. A total of 2,501 (30. 2%) of the 8,270 participants included in the analyses experienced 1 of the 3 cardiometabolic diseases, 511 participants met the criteria of multimorbidity, and 1,406 participants died over the follow-up period, with the mean age (SD) at death being 70.
As the Whitehall II study was designed to include a wide socioeconomic spectrum, with a 10-fold salary difference among the participants, the findings on socioeconomic factors are likely to be generalisable  [32]. Temporal changes in risk factor distributions over the course of the study are unlikely to affect generalisability  [39]  as we estimated risk factoroutcome associations rather than composite measures such as population attributable fractions that also incorporate information on prevalence of risk factors. All risk factors were assessed at age 50; changes in risk factor levels due to treatment or lifestyle modification were not examined in our study. As the data come from an observational study, the findings may be affected by unobserved confounders. Finally, we chose to model 5 transitions rather than all possible transitions between individual diseases and outcomes and between pairs of diseases in order to simplify the analysis and interpretation of results.
Acknowledgments We thank all of the participating civil service departments and their welfare, personnel, and establishment officers; the  British Occupational Health  and Safety Agency; the  British Council of Civil Service Unions ; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team. The Whitehall II study team comprises research scientists, statisticians, study coordinators, nurses, data managers, administrative assistants, and data entry staff, who make the study possible.
Data collection Data collected included age, sex and weight. Weights were measured using a battery powered portable Seca 216 digital floor scale and readings recorded to the nearest 0. 1 kg. Special care was taken that shoes and heavy cloths were removed before weighing. At the beginning of each day, scales were calibrated with a standard 5 kg weight and validated as accurate before use. Collected data were then entered into Microsoft Office Excel 2013 spreadsheets for windows and exported into the STATA software version 13. 0 for windows, for statistical analysis.
Data analysis From the recorded ages, estimated weights using APLS, Best guess and Nelson formulae were computed. Computed weights were then expressed as a percentage of the measured weight (100 Â estimated weight/measured weight). We standardized all measured weights to a value of 100%. Standardizing allowed for more accurate clinical relevance as a difference of 1 kg in a child who weighs 5 kg (20% error) is not of equal importance as a difference of 1 kg in a child who weighs 25 kg (4% error). Estimated weights within 10% (90-110%) of measured weights were considered clinically identical. Estimated weights <90% or >110% of the measured weights were classified as underestimation and overestimation, respectively. Differences between the proportions of clinically identical estimates from the different methods were compared using Cochrane Q test.