dataesr/scientific-paragraphs-categorization huggingface dataset:
550 to. 800) of Human Development Index (HDI) and at least one country with low HDI values (below. 550), as defined by United Nations  [29]. Data collection was initiated on March 24th, after obtaining ethics clearance (following the institutional regulations in each study country) and preparing 8 country/language versions of all study materials. Data collection continued until July 22nd 2020. Individual-level data were collected via web-based survey using Qualtrics platform. The questionnaire took approximately 15 min to complete. Snowball sampling was adopted as the main recruitment strategy, with social networks and university websites used to advertise the study. Links to the survey were posted online, together with information about the study aims and design. The only inclusion criterion was being ≥18 years old.
Next, adherence to the WHO handwashing guidelines across situations and information related to selected social-cognitive and sociodemographic variables were collected via self-report. The values of the index of strictness of containment and health policies, introduced by national governments in responses to COVID-19, were obtained from the Oxford COVID-19 Government Response Tracker database  [30]. The country-level data on strictness of policies, extracted for each week and each country of data collection, were matched with participants' data using dates and countries of data collection.
Indices of the trajectory of the COVID-19 pandemic (the predictor variables) The 6 indices of COVID-19 mortality and morbidity were extracted from Coronavirus Disease Situation Reports, published daily by the WHO for all countries affected by the COVID-19  [17]. Daily reports, published during the period from 25th March to 22nd July 2020, were extracted from the publicly available databases  [17]  for the 14 study countries and for each day during the period of the data collection. The following raw values were extracted from the WHO Coronavirus Disease Situation Reports  [17] : the number of new COVID-  19.
Sociodemographic variables (control variables) Data referring to country of residence, gender, age, marital status, education, and perceived economic status were collected. Participants indicated their marital status, which was coded as 0 (single, divorced, or widowed) or 1 (living with a partner, in a civil partnership, or married). Participants indicated their education level with responses representing the following 4 levels: primary school, vocational education or completed high school, ≤ 3 years of higher education, ≥ 4 years of higher education. Perceived economic status was measured with one item, 'Comparing to the average situation of a family in your country, what is the economic situation of your family?', with responses ranging from 1 (much above the average) to 5 (much below the average).
Country-level control variables Strictness of COVID-19-related containment and health policies was assessed with the index proposed by Hale et al. [30] , developed for between-countries comparisons. The values of the index are calculated for 180 countries for each week of the COVID-19 pandemic; data are publicly available from the Oxford COVID-19 Government Response Tracker database  [30]. The index is calculated as an additive of 8 containment polices (e. g. , restrictions of international travels, limits on gatherings, cancelling public events, schools and universities closed) and 6 health policies (e. g. , information campaigns on handwashing or social distancing, contact tracing after a diagnosis, use facial covering outside the home). Values of 2019 country-level indicators of HDI, capturing overall development, health, and educational situations were obtained from United Nations documents  [29].
Assuming small effect sizes of the COVID-19 indices on handwashing (ς 2 values between. 01 and. 006), an alpha level of. 05, power of. 99, and up to 13 predictors in the equation, the estimated sample size required to conduct the planned analyses was approximately 5800. Preliminary analyses (correlations, descriptives) were conducted with IBM SPSS 26. The multilevel regression analyses were conducted using the lme4 R package  [31] , R version 4. 0. 3 [32]  and maximum likelihood estimation procedures. Values of Akaike Information Criterion (AIC)  [33]  and Bayesian Information Criterion (BIC)  [34]  indicating prediction errors obtained for the main tested models were reported for further comparisons. Separate models were fit for each of the 6 indices of the trajectory of the COVID-19 pandemic, due to multicollinearity issues.
Acknowledgments We would like to thank the  Institut des Amériques  for funding this international research project as well as  Sorbonne Nouvelle University  and the  United Nations University Institute on Comparative Regional Integration Studies  ( UNU-CRIS ) for co-sponsoring two workshops (12-13 June 2017 in Paris and 4-5 October 2018 in Bruges) at which the papers in this Special Section were discussed. We are also grateful for the helpful reviewer comments on this article, to all the other reviewers involved in producing this Special Section, as well as to New Political Economy's Managing Editor  Colin Hay , for his quick responses and extensive advice and assistance.
The risk of adverse drug reactions was notably higher in patients who received off-label drug treatment [7. 3% vs. 1. 2%; P < 0. 01]  [5]. A study using an administrative database containing hospitalized patients aged 18 years or younger, from January 1 to December 31, 2004, from 31 tertiary care pediatric hospitals in the United States showed that at least 1 drug was used off-label in 297,592 (78. 7%) of 355,409 patients discharged during the study. Off-label use accounted for $270,275,849 (40. 5%) of the total amount spent on these medications. Therapie 76 (2021) 675-685 Medications classified as central or autonomic nervous system agents, fluids, nutrients, or gastrointestinal tract agents were the most commonly used off-label drugs, whereas antineoplastic agents were rarely used off-label  [6].
The observer followed one or several nurses for at least two hours with a questionnaire and noted all oral or injectable administrations. A total of 1,946 observations were recorded. The children were 12. 6 ± 17 months old, and weighed 8. 5 ± 9. 4 kg. From 35 to 77% administered drugs were offlabel in any formulations. In summary, this widespread use of off-label and unlicensed prescribing for children is reported all over the world. Clinicians argued that these prescriptions are almost inevitable, given the lack of RCTs for many drugs in children and the need to treat serious diseases or in urgent situation without alternative licensed drugs. However, children are not simply adults in miniature, and the extrapolation of adult data to children mostly based on assumptions. Drug dosages for different age groups are adjusted on the basis of weight or surface body.
Collection of patient data Patient data (age, date of birth, diagnoses, drugs administered, routes of administration, doses, and indications for use) from the hospital database of each center is sent each month for Lyon and every two months for Paris in the central database of the study (EREMI centralised database ClinInfo). The data could be viewed or adjusted using a standard electronical case report form (eCRF). Prescriptions for parenteral nutrition, standard crystalloid intravenous fluids, oxygen, and drugs used in research studies are collected but will not be considered. Automated calculation of estimated glomerular filtration rate (i. e. , creatinine clearance) using the modified Schwartz formula is implemented within the study database  [26]. Therapie 76 (2021) 675-685.
The primary reference sources for determining licensed indications are the product characteristic summaries of marketing authorizations in the Thériaque database www. theriaque. org (French-drug database)  [27]. Licensed drugs include medicines following the terms of the marketing authorization. An automated algorithm was developed to determine unlicensed and off-label drug use within the study database using information processed from Thériaque database. This algorithm classifies the use of all drugs under temporary use authorization as unlicensed. Off-label drug use is detected by the algorithm using Theriaque information on age, route of administration, renal contraindication (with the use of the calculated creatinine clearance), and contraindicated drug-drug interaction. One percent of prescriptions and all of the prescriptions related to any ADRs would be verified by a trained research team using a common classification system and validated by independent pharmacovigilance board (IPB) [see Fig.
Advantages and limits In the reported studies so far, the relationship between ADRs and unlicensed/off-label drugs was documented using the chart review method. The causality was assessed by pharmacoepidemiologists or pediatric clinical pharmacologist. Only in the study of Bellis et al. [4] , three investigators independently assessed causality for all possible ADR cases, agreement on causality category between all three investigators was taken as accepted consensus. In our study all adverse events are detected by the field specialist assisted by study teams using hospital databases and listing all drugs administered, all biological measurements and we have access to all medical records including routine clinical notes, vital signs and final medical report at discharge. Furthermore, experts in two independent pharmacovigilance centers, one in Paris and one in Lyon, assesse the reported ADRs.
Acknowledgments The authors would like to acknowledge the nursing and physicians staff in all departments of two pediatric hospitals -hôpital  Femme-Mère-Enfant of Lyon , and  Hôpital Robert Debré of Paris, France. We are thankful for the contribution of all collaborators:  C. Castellan ,  A. Portefaix ,  E. Javouhey ,  A. Lachaux ,  P. Cochat ,  S. Di Filippo ,  V. Desportes ,  M. Nicolino ,  P. Reix ,  P. Fourneret ,  X. Dode ,  A. Duclos ,  A. M. Schott ,  C. Carcel ,  D. Floret ,  O. Claris. We are thankful for the contribution of all experts of independent pharmacovigilance board:  Prof Daniel Floret ,  Prof Vincent Gadjos ,  Dr Jean Stagnara ,  Dr Jean Paul Langhendries ,  Dr A. Portefaix ,  Mrs Claire Guy ,  Mrs Celine Vilier  and  Mrs Nathalie Bleyzac  (pharmacists and pharmacovigilance experts).
Sigma-Mu applied to PROMETHEE II Assuming all necessary steps to apply SMAA-PROMETHEE II to a dataset are accomplished -that is: matrices RW, RP and RQ are constructed to compute matrices Φ + , Φ -and eventually Φ-, it is straightforward to compute the two parameters of interest, µ φ and σ φ for every alternative a i ∈ A as in equations (4. 2. 1. 6d) and (4. 2. 1. 6e) accordingly. These two parameters are based on the net flows, hence the regret factor is already taken into account in the intrinsic values implicitly -φ(a i , w mc , p mc , q mc ), ∀i = 1,. , n, mc = 1,. , s-and thus, in the parameters proxying their distribution, i. e. µ φ i , σ φ i. This is the fundamental difference with LP formulation (4. 2. 2. 5), which includes all components and thus the possibility to a more 'loose' trade-off among the two flows.
Materials Our stated preference study uses a Matlab application which automatically and successively generates path choice situations for the respondent. Each path choice situation corresponds to the general framework depicted in Fig. 1. The layout of the application is presented in Fig. 2 , where the different elements characterizing a path choice situation are highlighted: 1. The visualization of the OD pair with its three paths on the map. For this experiment, a total of 14 OD pairs were selected on the network of Lyon-Villeurbanne (France). The selection was made to cover a wide range of path configurations and trip distances (from around 2 to 10 km). Part of these OD pairs are taken from Gonzalez Ramirez et al. (  2019 ), and 4 representative pairs are shown in appendix. The OD pair proposed at each path choice situation is randomly picked from the OD set.
Design One of the critical parts of the experiment was the design of the travel time, CO 2 emission and congestion alleviation information. This was done to be as realistic as possible, while ensuring a wide variety of situations for the result analysis. For the travel time and CO 2 emission, the estimations given at each path choice situation are generated as follows. First, a mean value for P1 is used based on fixed mean speed and distance data for the current OD pair. For the car mode, the mean value was calibrated with Google Maps historical data in a preliminary study. The COPERT model  (Ntziachristos et al. , 2009; Lejri et al. , 2018)  was used to estimate the CO 2 emissions. For the public transport mode, the mean value came from the Lyon public transportation network estimations, available on its online platform.
For the second sub-question on emission reduction, Fig. 9 (a) presents the compliance in session 4 (solid line) as calculated with the social path (P3) choice frequency in session 4 (dashed line) and the observed choice frequency of P3 in session 3 (dotted line), see also Eq. 5. Here only three values of choice frequency are calculated for three intervals of travel time sacrifice, due to data sampling problems in obtaining reliable estimations of choice frequency. Notable differences from the previous sub-question case can be pointed out. As mentioned earlier, compliance is much lower in session 4 in comparison to session 2, due to the mode preference for public transport observed in session 3. Likewise, and as expected, compliance decreases with the increase of sacrifice, but the decrease is much slower when compared to session 2.
Acknowledgment The authors are thankful to all the lab members who helped the data collection by recruiting new participants and assisting them during the experiment. Special thanks to  D. Lejri ,  C. Buisson ,  N. Chiabaut ,  M. Seppecher ,  A. Takayasu ,  R. Chen ,  O. Rifki ,  M. Paipuri ,  M. Ameli ,  D. Ingole ,  C. Gomez  and  C. Veve.
This paper examines the effect of financial development in the Fintech age, measured by broad money, domestic credit, and mobile money, on poverty and human development in the Southeast Asian economies. Using unbalanced longitudinal dataset , the findings suggest that broad money and domestic credit contribute to poverty reduction and promote human development. The role of mobile money is seen to have a statistically positive impact only if we analyse it with human development. Additionally, when we take a closer look at the different stage of economic, political and institutional development in this region, we found that the positive effect of broad money and domestic credit is mostly found only in the less developed and less democratic countries. The mobile money, on the other hand, is found to statistically promote the human development in both groups of countries, but there is no statistical relationship for poverty analysis.
ε it is error term. The following is a detailed explanation on how we build our model. We start by constructing the following equation for each country: 1 it it it i t it Y Y finance u y α ρ β ε - = + + + + + (2) From the Equation (2), if we set the boundary of our analysis to only time-varying covariates and we assume that all the variables are truly exogenous, we may apply the one-stage least square estimations. However, there are many statistical issues, which we have to critically consider in order to obtain good estimations and will address in further detail below. To deal with this issue, we use instrumental variables and fixed-effects two-stage least squares for panel-data models. Staehr (2018) uses the country fixed effect model to study the relationship between capital flows and growth dynamics in 11 post-communist Central and Eastern Europe with the dataset from 1995 to 2015.
it it i finance finance finance finance = - + Where,. 1 1 Source: Author's estimation with database as outlined in Methods section. We applied Pairwise correlation between instruments and explanatory variables using pwcorr command in STATA. T i i it t finance finance n = = ∑ 1 1 1 T i it n i t finance finance n = = = ∑∑ Notes: Total natural resources rents (% of GDP) as NR, Employment in agriculture (% of total employment) as EA, Rural population (% of total population) as RP, Broad money (% of GDP) as M2, Domestic credit provided by financial sectors (% of GDP) as DC, Mobile cellular subscriptions (per 100 people) as MOBILE, and Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+) as MOBILE_$. Where n and N are the number of groups and the total number of observations of the variable, respectively.
Estimating the effect of financial development on poverty and human development We start our regression of financial development variables on poverty and human development, also including country fixed effects and dummies of the Asian financial crises in 1997 and the global financial crises from 2007 to 2009. The impact Source: Author's estimation with database as outlined in Methods section. We applied Pairwise correlation between additional control variables using pwcorr command in STATA. Notes: GDP growth (annual %) as GDP, government expense (% of GDP) as EXP, inflation consumer prices (annual %) as INF, and population growth (annual %) as POP. of financial crises on poverty and human development can be found in the following empirical and theoretical works  (Fiorentini, 2015; Jarrow, 2014; Mohseni-Cheraghlou, 2016; Nikoloski, 2011).
P-value, where *** p < 0. 01, ** p < 0. 05, and * p < 0. 1 denote significance levels. At the bottom of the regression results, the table shows First stage F stat, which is used to test for weak instruments. In case if p -value of First stage F stat is smaller than 5%, we obtain good instruments. Since most of the variables are estimated in logs, the coefficients are interpreted as elasticity from an economic point of view. Table  3  presents the formal regression analysis between financial development variables, using broad money (% of GDP), and domestic credit provided by the financial sectors (% of GDP), on the poverty and human development with the dataset from 1990 to 2017 in the Southeast Asian countries. The reported results present the satisfied R-squared. The number of observations for poverty declined importantly, compared to that of human development.
And the 2SLS first-differenced estimators and the GMM estimators are not done in this study. Another potential weakness is related to our dataset. In our research, there are many missing values, especially for poverty  used only this method. Last but not least, we also use mobile cellular subscriptions to study the role of digital financial innovation in Southeast Asian countries from 1990 to 2017. The mobile cellular subscriptions, which we have available data since 1990, is not relevant directly to digital financial innovation, while mobile money with direct impact has available data only from 2011. The literature estimates the mobile money as a dummy variable, using cross-section analysis method, to measure whether mobile money operates presented or not. However, it is not possible in our cases since mobile money operates in all countries in the sampling.
Acknowledgments This paper was written when I conducted my research at the  University of Bordeaux in France  and the  University of Lausanne in Switzerland. I am very grateful to  Prof. Bertrand Blancheton ,  Prof. Andreas R. Ziegler ,  Prof. Olivier Bargain ,  Prof. Alexandru Minea ,  Prof. Sandrine Mesplé-Somps ,  Prof. Tanguy Bernard ,  Prof. Yannick Bineau ,  Prof. Aaro Hazak , and  Prof. Kadri Männasoo  for helpful comments and suggestions. I also thank the editors for their patience and helpful suggestions.
486*** 0. 335*** 0. 369*** (1. 432) (1. 120) (0. 041) (0. 034) Country Fixed Effects Yes Yes Yes Yes Time 90-17 90-17 90-17 90-17 F test 17. 86 17. 92 44. 65 46. 04 Prob > F 0. 00 0. 00 0. 00 0. 00 R-squared 0. 45 0. 48 0. 77 0. 78 Observations 50 50 120 120 Source: Author's estimation with database as outlined in Methods section, using xtivreg command in STATA. Notes: *** p < 0. 01, ** p < 0. 05, and * p < 0. 1. Data in parentheses indicates standard errors.
373) (0. 011) (0. 011) Government expense -0. 417 -0. 365 0. 058*** 0. 067*** (% of GDP) (0. 364) (0. 308) (0. 011) (0. 010) DUMCRISIS 0. 201 0. 154 -0. 001 0. 0002 (0. 205) (0. 191) (0. 006) (0. 006) Constant 4. 413*** 4. 962*** 0. 305*** 0. 343*** (1. 660) (1. 217) (0. 046) (0. 037) Country Fixed Effects Yes Yes Yes Yes Time 90-17 90-17 90-17 90-17 F test 17. 23 17. 83 42. 34 41. 38 Prob > F 0. 00 0. 00 0. 00 0. 00 Hansen R-squared 0. 42 0. 47 0. 76 0. 76 Observations 50 50 116 116 Source: Author's estimation with database as outlined in Methods section, using xtivreg command in STATA. NOTES*** p < 0. 01, ** p < 0. 05, and * p < 0. 1. Data in parentheses indicates standard errors. financial.
685) (0. 018) (0. 036) Country Fixed Effects Yes Yes Yes Yes Time 90-17 14-17 90-17 14-17 F test 19. 19 12. 47 276. 94 199. 96 Prob > F 0. 00 0. 00 0. 00 0. 00 R-squared 0. 47 0. 30 0. 92 0. 68 Observations 51 23 125 42 Source: Author's estimation with database as outlined in Methods section, using xtivreg command in STATA. Notes: *** p < 0. 01, ** p < 0. 05, and * p < 0. 1. Data in parentheses indicates standard errors.
35 12. 48 172. 61 164. 46 Prob > F 0. 00 0. 00 0. 00 0. 00 R-squared 0. 25 0. 30 0. 87 0. 66 Observations 51 23 122 41 (POV6. 1) (POV6. 2) (HDI6. 1) (HDI6. 2) Source: Author's estimation with database as outlined in Methods section, using xtivreg command in STATA. Notes: *** p < 0. 01, ** p < 0. 05, and * p < 0. 1. Data in parentheses indicates standard errors.
Estimation approach The main tests are multivariate MIDAS conditional quantile regressions 3,4 to determine the sensitivity of operating performance to milk prices and dairy margins. These are estimated for the 5 per cent quantiles of operational performance from 0. 05 to 0. 95. To conserve space, in the tables we report the set: {0. 05, 0. 10, 0. 25, 0. 50, 0. 75, 0. 90, 0. 95}. Taking the example of the 0. 05 quantile, this means that we examine in a multivariate analysis the impact of milk prices and dairy product margins over the relevant operational period on the fifth-lowest percentile ranked semi-annual operational performance. The full range of quantiles considered gives us a holistic, distributional assessment of operational performance. Dairy data are incorporated into the model at weekly frequency for product margins and monthly frequency for raw milk prices.
| MEDIAN ESTIMATED BODYMASS INDEX OF UKRAINIAN CABINET MINISTERS IN 2000-2020 We collected 469 unobscured frontal face images of Ukrainian cabinet ministers who were in office in 2000-2020. In case of a cabinet reshuffle, when two (sometimes even three) individuals occupied the same ministerial position in the same year, we collected the image of the person who occupied office for the longest period of the year. The overwhelming majority of images were downloaded from two photobanks photo. ukrin form. ua and photo. unian. ua. Few missing photos were recovered from the websites of local Ukrainian media (fluency in Ukrainian was an asset). For each image in the data set, the minister's body mass index was estimated using the  Kocabey et al. (2017)  computer vision algorithm. Figure  3  plots the median estimated body mass index of Ukrainian cabinet ministers in 2000-2020.