dataesr/scientific-paragraphs-categorization huggingface dataset:
Extrinsic Evaluation of French L2 Vowels with Pillai Scores Since French L1 control data are available in the AixOx corpus, we used it to perform an extrinsic evaluation of French L2 vowel realisations. Although extrinsic evaluation is usually achieved via measures other than Pillai scores, it is entirely possible to use Pillai scores to measure the amount of overlap between realisations for a given vowel by one (or more) L2 learner(s) and a cohort of L1 control speakers. In this case, a smaller amount of overlap should indicate a closer-to-target (i. e. , more native-like) pronunciation. Therefore, we computed Pillai scores for realisations of the five marked French L2 vowels (/y/, /ø/, /Ẽ/, / Ã/, /Õ/) by each L2 learner, and the corresponding vowels produced by the 10 French L1 control speakers.
Acknowledgments: We are grateful to  Nadine Herry-Bénit ,  Audrey Gros-Bonfiglioli  and  Ioana Trifu-Dejeu  for sharing their data of 5 French participants with us. We also express our gratitude to the authors or the corpora used for analysis, and to  Caroline Bouzon  and  Marc Capliez , who performed a manual segmentation verification for the French L1 data of English L2 published in our initial exploratory study.
Data Availability Statement: This study analyses data from pre-existing speech learner corpora. English L2 data are part of the IPCE-IPAC corpus (https://www. pacprogramme. net/IPCE-IPAC), which is presently not freely available. French L2 data are part of three various corpora, some of which made be made available by authors (see Section 2. 2 for details about each corpus).
To this aim, we used audio processing software called DAVID  [22]  to apply a fixed set of programmable acoustical/emotional transformations to pre-recorded voice samples in two languages (French and Japanese), and performed cross-cultural voice recognition experiments to assess how identical cues were processed by speakers of both languages. In brief, DAVID alters the acoustic features of original voices by combining audio effects of pitch shift, vibrato, inflection, and filtering, to the result of modifying the emotional content of the original voice (Fig  1 ). In two previous studies with DAVID, authors applied real-time emotion transformations on participants' voices while they were talking. Transformed voices were perceived as natural emotional expressions, to the point that most participants remained unaware of the manipulation, but they nevertheless affected participants' emotional states and skin conductance responses  [23, 24].
DAVID was validated in multiple languages, finding that transformed emotions were recognized at above-chance levels when applied to either French, English, Swedish, or Japanese utterances, and with a naturalness comparable to authentic speech  [22]. In a series of two experiments, we manipulate here both Japanese (JP) and French (FR) voices with the same set of parametric transformations by DAVID, so as to display emotions of happiness, sadness, and afraid (Experiment 1) or to create sudden pitch shifts in the middle of a sentence without explicit emotional meaning (Experiment 2). Importantly, this procedure excludes the possible effect of cultural variability in how these cues were expressed, because target categories in the two languages were produced exactly in the same manner (i. e. , with the same algorithmic parameters).
Audio manipulation algorithm The audio recordings were manipulated with the software platform DAVID  [22] , available at https://forum. ircam. fr/projects/detail/david/. For the current experiment, we used predetermined pitch shift, vibrato, inflection, and filtering transformations designed to evoke happy, sad, and afraid expressions. Table  2  describes the transformation parameter values used in this study, which were applied identically to both Japanese and French-language stimuli. These values were based on previous studies  [22, 23]  that validated that they sounded both natural and recognizable in both JP and FR. Note that we modified some of the parameters from Rachman et al. [22] ; the pitch shift in the sad voices was set to -50 cents to mirror that in the happy voices. We also applied the same vibrato parameters to male and female voices because we considered that the original vibrato parameters would not have enough effect on some of the female JP voices.
Participants A total of 44 participants were recruited in Experiment 1. The participants consisted of 22 native Japanese (JP) speakers (9 female, M = 19. 7) and 22 French (FR) speakers (12 female, M = 23. 6). None of the JP speakers had ever learned French, and none of the FR speakers had ever learned Japanese. Experiments with the Japanese speakers were conducted in the University of Tokyo (Japan), while those with the French speakers were conducted at the INSEAD/ Sorbonne-UniversiteBehavioural platform in Paris (France). Volunteers were recruited through local databases and mailing lists and were financially compensated for their participation. Two French participants were excluded because they did not satisfy language requirements (not native FR speakers). Furthermore, one Japanese participant was excluded because they reported that they could hear the auditory stimuli only from one side of the headphone.
Data analysis We computed the mean hit rate (biased hit rate, H b ) of the three emotion categories (happy, sad, and afraid) for each of the nine conditions (JP, FR: normal, jabberwocky, shuffled, reversed; SE: normal). To take a possible response bias into account, we also calculated unbiased hit rates (H u ) and chance proportions (p c ) for each participant  [29]. Unbiased hit rates take a value between zero and one and take into account how often an emotion is identified correctly, as well as the total number of times that an emotion label is used. Both biased and unbiased hit rates are reported in the Results section, but the unbiased hit rates were submitted to paired t-tests to assess performance against chance level and to mixed ANOVAs to test for effects of language and sentence type.
Stimuli Stimuli were based on the 64 non-manipulated French and Japanese sentences used in Experiment 1 (2 languages × 4 sentences × 4 conditions × 2 speakers). We used the pitch-shift subcomponent of DAVID to create sudden positive pitch changes at a random time point between 400 and 600 ms after sentence onset, and asked participants whether they could detect their occurrence. The size of the shift, between +14 and +257 cents, was calibrated with an adaptive procedure (see Procedure below).
01). Participants' performance (Happy: native FR pi =. 66, native JP pi =. 80; Sad: native FR pi =. 68, native JP pi =. 64, Afraid: native FR pi =. 58, native JP pi =. 65; pi: proportion index, see Materials and Methods. ) was comparable with a previous study using DAVID (Happy: pi =. 66, Sad: pi =. 71, Afraid: pi =. 70) [22]. Together these results show that participants had good emotion recognition abilities in their native language and that the emotional transformations are appropriate in both languages. To test for language-familiarity effects, we then examined unbiased hit rates on cross-cultural stimuli. In this analysis, we averaged unbiased hit rates in the three emotional categories (happy, sad, afraid). The neutral condition was not included in this analysis because this condition did not contain any emotional transformation.
This work was supported by  ERC   Grant StG 335536 CREAM, Fondation pour l'Audition FPA RD-2018-2, ANR REFLETS and SEPIA  to  J. J. A. ,  JSPS KAKENHI  ( 20H05023  and  20K07718 ) and  H2020 Marie Skłodowska-Curie Actions  ( 101023033 ) to T. N. ,  JSPS KAKENHI   Grant-in-Aid for Scientific Research on Innovative Areas  (# 4903 ; Evolinguistics) to K. O. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We use data from the PISA study to establish a validity argument for such use based on theoretical expectations about relations to other variables. First, we evaluate to what extent the behavioural measures exhibit evidence of convergent validity  (Fiske, 1971, p. 164) : we expect behavioural measures to be significantly correlated with self-report measures that were developed to assess the PSEC construct. Second, we evaluate whether behavioural measures present similar differences across population sub-groups (i. e. gender, socio-economic status) as those observed through the use of self-reported measures, and examine the relationship of both groups of measures with performance in the PISA achievement test. For a subset of countries, we evaluate the evidence for predictive validity of the behavioural measures: the measures are expected to be correlated with future education outcomes, such as completion of higher education, that are influenced by socio-emotional skills  (Danner et al.
, 2021). We also compare behavioural measures and self-report methods according to whether measures based on different survey rounds provide consistent conclusions, and conduct tests to identify how sensitive different instruments are to the use of different administration protocols or instruments. Prior work in psychology has identified low correlations between self-reports and task-based performance measures as well as low correlations between different task-based performance measures of socio-emotional constructs (see for examples  Duckworth and Kern, 2011; Sharma et al. , 2014; Cyders and Coskunpinar, 2011; Allom et al. , 2016). However, no study to date has systematically examined the correlation between behavioural measures that are routinely used in the economics of education literature and that are developed using data from large-scale international assessments and self-reports routinely employed in these assessments.
Data We use data from PISA. PISA is a triennial large-scale international assessment of 15-year-old students conducted since 2000 and targeting the schooled population of children between the ages of 15 years and three months and 16 years and 2 months at the time of administration (15-year-olds from now on). It covers large, representative samples of 15-year-old students and over 80 countries have participated at least once since the first edition was conducted in 2000. 2 PISA is a low-stakes assessment at least at the individual level because no individual scores are released to students or schools. Therefore, intrinsic motivation plays a considerable role in guiding the behaviour of participants in the study. In the absence of external pressure and motivational drivers, variability in motivation is higher than in the presence of external incentives and the influence of PSEC on performance tends to be stronger  (Barry et al.
Most analyses are based on the PISA 2012 study. While more recent editions of PISA are available, 2012 is the only edition that contains design features that allow to validate alternative operational definitions of the behavioural measures and test the robustness of behavioural measures to different administration conditions. We use PISA 2003 data to examine the stability of measures over time and PISA 2000 for Canada, Denmark and Switzerland and PISA 2003 for Australia to examine how predictive measures are of educational attainment at age 25. In these countries, PISA participants were followed in national longitudinal studies. The follow-up data for Denmark is derived from PISA and the OECD Survey of Adult Skills (PIAAC). For Switzerland, we used data from the Transitions from Education to Employment (TREE1) surveys.
For Australia, we used data from the Longitudinal Study of Australian Youth (LSAY). For Canada, we used data from the Youth in Transition Survey (YITS). Canadian data have not been released for public access, so all estimates were obtained through collaborations with national researchers at Statistics Canada, who conducted the analyses on the basis of statistical programmes prepared by the authors. While the frequency and timeline of follow-up surveys vary by country, all the datasets collect information on young people when they were approximately 25 years old (the only exception is Denmark, where the analysis refers to individuals who were either 26 or 27 years old). Table  B1  in the Supplementary Online Annex provides additional details on the longitudinal studies.
The gender gap refers to the difference between and males. A negative number implies that females have lower mean values than males. A positive value implies that females have higher mean values than males. An asterisk (*) next to an indicator name indicates that the estimated d value is statistically significant at least at the 5% level. Data for all countries in the sample can be consulted in Table  H2  in the Online Supplementary Annex. Source: PISA 2012data.
A positive value implies that advantaged have higher mean values than disadvantaged students. An asterisk (*) next to an indicator name indicates that the estimated d value is statistically significant at least at the 5% level. Data for all countries in the sample can be consulted in Table  I2  in the Online Supplementary Annex. Source: PISA 2012data. controls. Coefficients associated with the PSEC indices measure the change in the probability of completing upper-secondary education and university by age 25 as a function of a change in one standard deviation in PSEC indicators. 7 The results indicate that performance decline is associated with university completion in three out of four countries: in Australia and Switzerland, students with similar characteristics and performance who had a one SD greater decline in performance in the PISA test in 2000 had a 7 percentage point lower probability of completing university by age 25 and in Canada they had a 13 percentage points lower probability.
A B S T R A C T Background: Physical abuse often begins at a very young age and sometimes results in serious or fatal injuries. It is crucial to diagnose physical abuse as early as possible to protect this vulnerable population. Objective: To study the factors associated with the first hospitalization for physical abuse from birth to the infant's first birthday in France. Participants and setting: We included all singleton children born in a hospital setting in France between 2009 and 2013, who were identified from the French national information system database (SNDS). Methods: To study factors associated with the first hospitalization for physical abuse during the first year after birth, we used the Fine and Gray regression model. Factors included in the multivariate model were the infant's sex, prematurity, neonatal conditions, the number of hospitalizations (at least two), medical consultations and complementary universal health insurance (proxy for family precariousness and socio-economic vulnerability).
Databases The SNDS (Système National des Données de Santé) is the French national information system that contains individual, extensive, and linkable but anonymous data on healthcare use for approximately 77 % of the French population during the study period  (Tuppin et al. , 2010)  and almost 99 % nowadays  (Bezin et al. , 2017). It aggregates data from the following sources: (a) The hospital discharge abstract database (Programme de Médicalisation des Systèmes d'Informations [PMSI]), which is designed to include discharge abstracts for all inpatient admissions to public and private hospitals in France. Diagnoses identified during the hospital stay are coded according to the 10th edition of the International Classification of Diseases (ICD-10). The very good quality of this database has previously been evaluated, making it possible to conduct epidemiological studies involving hospitalized patients in France  (Maitre et al.
Design and population We constituted a longitudinal cohort study using the SNDS. From this database, we identified all singleton children born in a hospital setting in France between 2009 and 2013. The population of singleton children included in the SNDS is almost complete: in France, almost all deliveries are recorded in the PMSI database, because out-of-hospital delivery is rare, accounting for only 0. 4 % of all births  (Blondel et al. , 2011).
Ethics This study was carried out by a multidisciplinary team including epidemiologists with expertise in the SNDS database, forensic physicians, psychologists and psychiatrists. The work was approved by the French Institute of Health Data (INDS, Institut National des Données de Santé, registration number 218, December 1, 2016) and authorized by the French Data Protection Authority (CNIL, Commission Nationale de l'Informatique et des Libertés, registration number DE-2017-270, June 14, 2017). Written consent was not needed for this longitudinal study as there was no impact on patient care and all data were anonymous.
Between 2009 and 2013, we identified an annual incidence of infants hospitalized for physical abuse of around 26 infants per 100,000 singleton births per year (i. e. around 0. 026 %), which may seem low compared with the rates in the literature  (Diaz & Petersen, 2014; Gessner et al. , 2004; Leventhal et al. , 2012; Sibert et al. , 2002) , which are substantially higher and with rates >50 infants per hundred thousand births per year. However, these studies were based on classifications  (Leventhal et al. , 2012)  other than ICD-10 codes, other sectors of activity  (Sibert et al. , 2002)  or used several different databases in order to be as exhaustive as possible  (Gessner et al. , 2004). According to Gilbert et al. , 10 % of infants in high-income countries were neglected or abused  (Gilbert et al.
This complementary insurance is a specificity of the French health system, which reimburses medical expenses not covered by social security. In our study, the data concerning this complementary insurance was collected administratively and are therefore exhaustive and reliable, allowing us to identify the most severely disadvantaged families. There is growing evidence of an association between low socio-economic status and child maltreatment, as shown in the study of Sidebotham or the meta-analyses by  Mulder et al. or Van IJzendoor et al. Moreover, a 2009 study conducted in China (Wong et al. , 2009)  showed that the risk of physical abuse was higher in patients whose family had a lower socio-economic status. According to the literature  (O'Donnell et al. , 2010) , abused children had twice as many hospital stays (excluding the birth stay and the stay identified as abuse) as children without abuse.
Having been hospitalized at least twice was a factor associated with the occurrence of physical abuse  (Lyons-Ruth & Yarger, 2022). This finding is not surprising given the difficulty of diagnosing maltreatment without stigmatizing the child, or the medical nomadism of these families. This study has multiple strengths. First, the SNDS database on which the study is based is unique in Europe and even in the world, constituting a considerable advance in the analysis and improvement of health for the entire population. It allows us to obtain a complete follow-up, whether in or out of the hospital. Moreover, the population studied is almost complete since, as mentioned above, only 0. 4 % of deliveries are not recorded  (Blondel et al. , 2011). Our team has considerable experience using the SNDS database for epidemiological purposes and in the construction or the validation of algorithms to identify people suffering from certain diseases or health events  (Goueslard et al.
, 2020; Hanf et al. , 2013; Maitre et al. , 2021; Piroth et al. , 2021; Quantin et al. , 2012 Quantin et al. , , 2021) )  including in the context of child abuse  (Paget et al. , 2020). Thus, in a previous study supported by the Observatoire National de la Protection de l'Enfance (ONPE), we developed algorithms for the identification of cases of physical abuse in children under one year in France  (Gilard-Pioc et al. , 2019). We were able to validate this algorithm with a positive predictive value of >90 %  (Loiseau et al. , 2022). Our study also has some limitations. The appropriateness of using hospital data to identify cases of physically abused children could be questioned. However, a large proportion of abused children will be hospitalized at least once in their first year of life and will therefore be recorded in the PMSI database because situations of child abuse or trauma are serious medical conditions that are difficult to ignore in a hospital discharge summary.
Another limitation concerns analyses based on data from 2009 to 2013 only, because we were authorized by the National Committee for data protection (CNIL) to carry out our analyses on these years specifically and were unable to extend to data from more recent years. Finally, our study only included singletons because we could not easily identify newborns from multiple pregnancies separately in our database, especially when they were the same sex. Newborns from multiple pregnancies are given the same unique number and it is therefore not possible to identify their follow-up separately in the various hospital stays. Overall, there is a need for early detection of at-risk situations, particularly in cases of prematurity or precariousness. In the case of premature birth, families could be offered psychological support, aiming to help the parents accept and better manage their child's issues.
Study population General practices were drawn at random from the list of GPs of the Auvergne-Rhône-Alpes Regional Health Agency, and parents of children who received medical care from the selected GPs were requested to participate to the study. Random selection was made using Stata software. The objectives and modalities of the study were presented to the GPs by telephone call before obtaining their oral agreement to participate. No response after three telephone calls was considered a refusal. Thirty questionnaires per GP were distributed. They were offered to parents by the medical secretaries or left directly in the waiting room. A collection box was made available for the participants to deposit the completed questionnaires. An information note was given to each parent, explaining the objectives of the study, the conditions of anonymization and the confidentiality of the data collected.
Questionnaire We designed a questionnaire with 32 questions divided into 3 parts. It was based on the latest position statements of the French Paediatric Society (SFP)  [24]. To evaluate its comprehension and acceptability, the questionnaire was pretested with 30 parents with different sociodemographic characteristics. It was composed of the following 3 parts: -The first part included sociodemographic data about the parents: age, sex, family situation, level of education, socio-professional category, and place of residence. Two questions asked the parents about their child's daily screen time during the week and on the weekends. -The second part was concerned with screen equipment in the child's home, including in their bedroom. TV screens were distinguished from other multimedia screens (smartphones, computers, video game consoles, tablets).
-The third part included questions about the child: month and year of birth, sex, the presence of older siblings, and the number of children in the household. Two questions were designed to estimate the child's average daily screen time (ADST), both during the week and on the weekends. The parents were also asked to identify the total amount of screen time on the day before the survey to obtain greater precision and to check the consistency of the responses. Data on the children's exposure times and parental guidance were collected. A 6-point scale was used for the responses (never, rarely, sometimes, regularly, often, always). Each of the quantifying adverbs was associated with a temporality for a better objectivity of the answers (never, 1 to 2 times per month, 1 to 2 times per week, 3 to 4 times per week, 5 to 6 times per week, every day).