dataesr/scientific-paragraphs-categorization huggingface dataset:
S2  for details). These filters were: (i) four dynamic filters derived from Exp. 1 (both dominance and trustworthiness kernels and their opposite patterns multiplied by 1,050 cents); and (ii) three other static filters, used as control: a baseline condition with no change and two constant-gain filters calculated as the mean value of the kernels from Exp. 1. On these three constant kernels, we applied the same amount of random pitch fluctuations as the kernels of Exp. 1, to avoid stimuli with unrealistically flat intonations; this also allowed us to replicate reverse-correlation analysis on this new set of data (see below). The value of 1,050 cents (=15 SD used in the task) was chosen to generate pitch changes with peaks ∼250 cents. Comparison across conditions analyses. For each subject and each stimulus, we computed the mean rating in each condition and divided this value by the mean rating collected in the baseline (no change + noise) condition.
ACKNOWLEDGMENTS. The work was supported by European Research Council Starting Grant ERC CREAM Grant 335536 (to J. -J. A. ); and by Institute of Language, Communication and the Brain Grant ANR-16-CONV-0002, Brain and Language Research Institute Grant ANR-11-LABX-0036, and A*MIDEX Grant ANR-11-IDEX-0001-02 (to P. B. ). Funding was also provided by Grants ANR-10-LABX-0087 IEC and ANR-10-IDEX-0001-02 PSL* from the Agence Nationale de la Recherche. Data were collected at the Centre Multidisciplinaire des Sciences Comportementales Sorbonne Universit és-INSEAD.
Method Research design and sample. To be consistent with past research, we compared complete DSCs with incomplete DSCs to control for the number of arguments used by the employee (see  Bonifield and Cole 2008 ; Study 2). We did not expect that this difference would impact our findings because both complete and incomplete DSCs should be perceived manipulative and derogatory according to our theory. We conducted a 3 (DSC: Complete DSC vs Incomplete DSC vs No DSC) X 2 (Apology: Intense vs Weak) between-subjects experiment. Three hundred and nine British residents were recruited for participation using the consumer panel Prolific Academic (https://www. prolific. ac). Recent research shows the suitability of this platform for behavioral research  (Peer et al. 2017). Participants accessed an online survey through a link and were randomly allocated to the relevant condition by the survey software Qualtrics.
Acknowledgments: We would like to thank  Luc Bauwens  for penetrating discussions as well as  Mohammad Abu-Zaineh ,  Thibault Gajdos  and  Stephen Bazen. This paper was presented in various places: Marseille, workshop on Multidimensional Inequalities, May 2015; Orleans,  French Econometric Conference , December 2015;  Jiangxi University of Economics and Finance , April 2016; Rimini, 10th Annual RCEA Bayesian Econometric Workshop, May 2016. We would like also to thank two anonymous referees for their useful comments and suggestions. Of course, remaining errors and shortcomings are solely ours. This paper was written at the occasion of the visit of Tareq Sadeq in Marseille during the year 2014-2015 financed by a European mobility grant.
It aims to build a solid two-way bridge between, on the one hand, theory, and, on the other hand, data concerning learning and intellectual performance. "Two-way" implicates that (a) the theory is strongly constrained by and built out of observation, but also that (b) the theory can "project" directly onto what learners actually do as they think and learn, giving general meaning to their actions. KiP is, thus, a reaction against theories that are a priori, very high level, and consequentially are difficult to apply to the messiness of real-world learning. KiP shares important features with two major progenitors. The first is Piagetian and neo-Piagetian developmental psychology, epitomized in mathematics education by Les Steffe, Ernst von Glasersfeld, Robbie Case, and many others. 1 While I provide specific hints later for more detailed comparisons on a per-topic basis, probably the most effective single hint I can provide for reader-developed comparisons is to consider (a) whether work on the same topic identifies intuitive pre-cursor ideas in detail (few do), including their productive as well as problematic nature, and (b) whether data analysis includes extensive examination and explanation of students' in-process thinking, in addition to long-term comparisons.
2. Among individuals, variations in local structure according to individuals. This type of data analysis allows us to identify in-depth profiles of students with varied characteristics: gender, achievement in mathematics, beliefs, display style, and emotions. The affective pathways they reported were compared in order to glean information on meta-emotion and visualization. The comparison revealed: (a) the use of visualization and associated emotion and (b) the dependence of their emotional self-control on their individual perception, which was influenced by style, disposition, type of activity or skill, instrumental command, and belief system around technology-aided mathematical learning. For more detailed information see  Gómez-Chacón (2012 , 2015).
Implicative Data Analysis In this research, together with the qualitative analysis (Sect. 10. 3. 2), an implicative analysis  (Gras et al. 1997 ) was performed in order to explore the structure in cognition and affect interactions for the group. Gras's implicative statistical method has been conducted by using software called CHIC (Classification Hiérarchique Implicative et Cohésitive). At the descriptive level, it can be used to detect a certain degree of stability in the structuring, while for predictive purposes, it provides the grounds for assumptions. This statistical analysis was then used to establish rules of association for data series in which variables and individuals were matched in order to define trends in sets of properties on the grounds of inferential, non-linear measurement.
In that way, a country will get a much broader view of their own educational system, and better information about how to improve education in their country. Researchers in Finland have warned about the problem Finland is facing because they mostly only participated in PISA: A proper mathematical basis is needed especially in technical and scientific, biology included. The PISA survey tells very little about this basis, which should already be created in comprehensive school. Therefore, it would be absolutely necessary that, in the future, Finland would participate also in international surveys which evaluate mathematical skills essential for further studies. (Astala et al. 2005 , https://matematiikkalehtisolmu. fi/2005/erik/ PisaEng. html) International comparative studies have produced a large databank that can be used to answer a high number of interesting research questions.
A new EC was elected for a  3-year term, 2010 -2012 , at ICME-11 in Monterrey (2008)  with Bill Barton as President and Jaime Carvalho e Silva as Secretary-General. Under their leadership, ICMI Studies 20 and 21 were conducted: • Educational Interfaces between Mathematics and Industry (Lisbon, 2010), organised jointly with the International Council for Industrial and Applied Mathematics (ICIAM) • Mathematics Education and Language Diversity (Águas de Lindóia, 2011). ICME-12 was held in Seoul in 2012, and the Klein Project underwent considerable development. 10 Moreover it was decided at the 2011 EC meeting to launch the Database Project, with the ultimate goal of building a free access database of mathematics curricula from all over the world. This EC also saw, in 2011, the inauguration of the IMU Secretariat in Berlin, where a position of ICMI Administrator had been established.
Formative Assessment and Teachers The teachers reflected on their role, which they found even more important and difficult than they foresaw. The main problems they identified were time and resources demands (worksheets, assessment tools, and teacher assistant time). This is related to the issue of appropriate support that teachers need. At the beginning of experimental teaching, the teachers appreciated the worksheets for pupils. During and after the experiments, they reported that they preferred to see a more experienced colleague's teaching, real teaching-situation stories, a databank of tasks, training courses for teachers, and researchers' on-site support. Some training, therefore, should precede the implementation of formative assessment, and assistants in classes would also be helpful, as the implementation would be time consuming and not all teachers have proper readiness.
Each of the first 99 pages contained a list of about 10 publications (articles, books, conference proceedings papers, etc. ). The last page only had one publication listed on it. Table  22. 1 shows the total number of journal articles that spoke to research on mathematics teacher professional development in South Africa for the 10-year period 2006-2015. I repeated the same procedure for the other search phrases and specifically looked for articles that had not been prompted by the preceding searches. Secondly, I searched for publications in South African databases (e. g. , Sabinet and Ebscohost) for accredited journals. I excluded conference publications to narrow down the search and ensure only articles involving more rigorous peer review were included. I excluded book chapters, as most appeared to be drawing from prior published journal articles or unpublished thesis work.
It is also worthwhile noticing that this question is related to Generic Skill 2, active learning and practical application. Question CC: Comprehension of Concepts (CC). "The professor facilitated the understanding of the content through clear explanations. " It is mentioned in the mathematical section of this paper that the introduction of the systems perspective and the use of a simulator such as Vensim is to allow the students to have another representation (the fifth representation: stock-and-flow diagrams) of the DE for them to have better understanding of this math concept. Question RDL: Research Documents in Library (RDL). "The professor promoted the use of the query library materials (books, magazines, digital library, and databases) to support learning activities. " This question is about how to deal with complex problems, beyond those traditionally seen in a math class; therefore, there is the need to search information in other sources in addition to the one found in the textbook of the math class.
Values and Valuing Driving Performance Research evidence has also supported the belief that mathematics performance is related to students' valuing. In addition to the Nuffield Foundation-commissioned report  (Askew et al. 2010 ) mentioned above which highlighted the role of cultural values, there is also more recent research by  Jerrim (2014) , who sub-divided the Australian dataset for PISA 2012 by broad student ethnicity, specifically, high-performing East Asian, low-performing East Asian, Indian, British, and native Australian. Given that the students in the sample were second-generation immigrants experiencing an Australian mathematics education with their native Australian peers, it can be assumed that the factors underlying the differences existed beyond the school level, with their different emphases and valuing on different aspects of school education.
Chapter 43 Equity in Mathematics Education: What Did TIMSS and PISA Tell Us in the Last Two Decades? Yan Zhu Abstract Equity in education has been a concern of almost all countries, whether developed, transitional, or in the progress of developing. It is believed that unequal education implies that human potential is being wasted. The present study focused on students with different characteristics as aggregate groups in an examination of similarities and differences in mathematics learning. The information analyzed here was mainly based on data from TIMSS and PISA databases. This investigation aims to paint an overall picture about gender equity, socioeconomic status, and indigenity equity in mathematics education over the last twenty years. It is hoped that the study can provide useful insights to individual education systems and further help them to identify more promising practices to narrow or even eliminate the existing between-system as well as within-system gaps.
For instance, in PISA 2009 it was found that only 3. 4% of girls compared to 6. 6% of boys were at the top performance in mathematics literacy. Based on both the TIMSS and PISA databases, Baye and Monseur (2016) analyzed gender differences, from an international perspective, from 1995 to 2015 via the use of effect sizes and variance ratios. The results showed that the sizes of the gender differences varied according to student proficiency levels. In particular, at the lower tail of the distribution, effect sizes were close to zero or in favor of girls, while systematically at the upper tail, boys were more proficient. The largest gender difference in mathematics literacy was observed on PISA 2003 for the most proficient students (i. e. , percentile 95). Baye and Monseur claimed that such a tendency was more obvious at the secondary level of education, and in PISA rather than in TIMSS.
Acknowledgements This work was supported by the  Teaching and Learning Research Initiative  fund administered by the  New Zealand Council of Educational Research. I am indebted to my Acknowledgements Some of the work in this paper was funded by the  National Science Foundation  under grants  ESI 9901275  and  ESI 0424983  and by the  Heising Simons Foundation , Grant # 2016-065. The views expressed here are those of the author and do not necessarily reflect the views of the funding agencies. Acknowledgements The research reported in this paper was funded by the  Australian Government  as part of the  Bridging the Numeracy Gap in Low SES and Indigenous Communities Project. The author acknowledges gratefully the contribution of the research team and all participating teachers, parents, students, and school communities.
Acknowledgements The paper's elaboration has been supported by a special action grant,  EDU2013-44047-P , from the  Government of Spain  and a research grant  Visiting Scholar Fellowship ,  University of California, Berkeley. Acknowledgements This research was supported by the  Capacity , Collaborative Project no. 321428 , entitled " Assess Inquiry in Science, Technology,   and Mathematics Education"  (under the  Seventh EU Framework Program ) and the  Czech Science Foundation  under project  Fig. 15. 5  The process of  formation of autonomous assessment (Source Hošpesová and Žlábková 2016 ) Acknowledgements This study was supported by the  Israel Science Foundation , Grant # 1539/15 , and by the  Israel Trump Foundation for Science and Mathematics Education , Grant # 7/143. I wish to thank my colleague and friend  Abraham Arcavi , with whom I co-direct the VIDEO-LM project, for a most valuable and productive partnership.
Acknowledgements The author is indebted to  T. Chehlarova  and  E. Sendova  for the longstanding collaboration in promoting the mathematics with computers idea and for the fruitful remarks and suggestions related to this paper. In particular, the final version of the title of the paper was suggested by  E. Sendova. The author is indebted also to  Dj. Kadijevich  for pointing some relevant literature sources. Special thanks are due also to anonymous reviewers whose critical notes helped clarify and improve the paper. Acknowledgements I am grateful to the Program Committee of ICME-13 for the invitation to give a talk at the conference. The empirical example presented in this chapter is taken from a study supported, in part, by the  Israel Science Foundation  (grant#  1596/13 , PI B. Koichu).
The opinions expressed here are those of the author and are not necessarily those of the Foundation. Acknowledgements Writing of this paper was supported by the  InforMath  project, funded by the  National Science Foundation  through grant  DRL-1323587. All opinions and analysis expressed herein are those of the author and do not necessarily represent the position or policies of the Foundation. This paper is the result of collaborative work with  Cierra Rawlings  and  Bohdan Rhodehamel  at  San Diego State University , and  Molly Kelton  at  Washington State University. Due to the ICME rules for invited lectures, they have not been included as co-authors. I want to thank them for their contribution and for their agreement to the latter. I wish to thank the  Mingei International Museum , the  Boys and Girls Club of Chula Vista , and the  Math Technology Lab at San Diego State University.
Acknowledgements This research was supported by a  Multi-Year Research Grant  from the  University of Macau  ( MYRG2015-00203-FED ). I am greatly indebted to  Maria G. Bartolini Bussi  for critical feedback and for help in framing of the ideas and perspectives presented here. Acknowledgements A sincere acknowledgement goes to the  Department of Mathematics at Stanford University  for hosting me over the last four years as I worked on the pedagogy of mathematical learning project. Also, special thanks to  Jo Boaler ,  Professor  of  Mathematics Education at Stanford University , and  Shima Salehi , a Ph. D. student in the  Graduate School of Education at Stanford University , for their valuable advice. Acknowledgements The research reported in this paper has been financially supported by project  PROGRES   Q17 , " Teacher Preparation and the Teaching Profession in the Context of Science and Research ".
MeThods The Whitehall II cohort study was established in 1985, based on 10 308 civil servants (3413 women) aged 35-55 years recruited from 20 offices in London. The response rate was 73%. 14 Data collections involving completion of a self-administered questionnaire and a clinical examination were made approximately every five years. 14 We analysed data across four 5-year periods between 1991 and 2013 (table 1). Participants were followed into retirement, regardless of their employment history, and eligible to be included in a period if they completed the Short-Form 36 (SF-36) questionnaire at beginning and end of the period. All participants provided informed consent.
outcome The SF-36 was administered on five occasions. The 36-item questionnaire has eight scales: physical functioning, role limitations due to physical problems, bodily pain, general health, vitality, social functioning, role limitations due to emotional problems and general mental health. 15 16 The scales can be summarised into mental component score (MCS) and physical component score (PCS) based on factor analysis to produce two scores scaled Inequalities from 0 to 100 (high score indicating good health) with mean 50 and SD 10, using the US population as reference. 17 18 MCS and PCS differ by age and sex, and we used data from all five phases to generate age-sex-specific cut-points in order to define poor health as the lowest 20% of the distribution (online supplementary tables S1 and S2).
Variables The socioeconomic status of each participant was verified in each period, based on last known (current or most recent) civil service employment grade. Employment grade was assigned by combining official occupational grade into three categories: high (administrative), intermediate (professional or executive) and low (clerical or support officer). Employment grade classifies the cohort by salary, job characteristics such as control over work content and opportunity for use of skills, and social status. 14 ealth-related behaviours (smoking, alcohol intake and physical exercise) and health status (BMI, long-standing illness, coronary heart disease (CHD), stroke, diabetes mellitus, systolic blood pressure (SBP) and serum cholesterol) were derived from self-reports and clinic data at the beginning of each period.
statistical methods The analytic sample was defined as person-observations with poor physical or mental health at the beginning and a valid SF-36 score at the end of any of the four periods. We summarised risk factors at the start of each of the four periods. We imputed missing risk factor data using multiple imputation which replaces missing values with randomly selected draws from the missing data distribution conditional on the observed data, specified by an imputation model, creating multiple imputed datasets. 19 Multiple imputation accounts for uncertainty due to missing data and obtains unbiased estimates and SE if the missing at random assumption is plausible, that is, the reason for the missing data is associated with observed, but not unobserved, data. 19 The imputation model included the risk factors described earlier, MCS and PCS and the following auxiliary variables which were associated with at least one of the risk factors with missing data.
20 The risk factors were total cholesterol, SBP, fruit and vegetable consumption, chest pain, angina, high general health questionnaire score and receiving antihypertensive drug treatment at the beginning of the period and family history of angina, heart attack, stroke, high blood pressure or diabetes. We used 10 cycles to impute missing values in each imputed dataset and generated 20 imputed datasets which were analysed separately and the results combined using logistic regression models for recovery from poor physical and mental health, adjusted for age and sex, for each period separately. Socioeconomic gradients in recovery did not differ across periods; therefore, the four periods of observation were combined. We fitted logistic regression models for all periods combined including period as a stratification variable in the models.
B. K. is supported by the  Wellcome Trust  ( 204862/Z/16/Z ). C. L. M. and F. M. S. are supported by the  Wellcome Trust  ( 108440/Z/15/Z ). L. L. receives support from the  Agence Nationale de la Recherche  (grants  ANR-16-CE35-0004-01  and  ANR-17-ERC2-0016-01 ), the  French Government 's  Investissement d'Avenir program Laboratoire d ' Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID ), the  City of Paris Emergence(s) program in Biomedical Research , the  European Union 's  Horizon 2020 research and innovation programme under ZikaPLAN  grant agreement No. 734584 , and the  Ecology of Health program  of  Centre National de la Recherche Scientifique  (grant AFRICAEDES). A. B. is supported by a  WHO/TDR  grant ( RCS-KM 2015 ID235974 ). P. J. M. receives personal support from The  Medical Research Council  (Grant No.
This analysis yielded average absolute inter-annual variabilities of 0. 61‰ and 1. 24‰, respectively; proving low sample replication is likely not a cause of the increase, yet is still a limitation in the dataset. Despite the initial significant, short-lived sourcewater depletion at the BCP boundary, change point analysis (Methods) suggests five distinct δ 18 O* SW change phases; the first in variability (12 702 cal BP) and the following four in mean (>0. 5‰: 12 664, 12 646, 12 616, 12 608 cal BP) (Table  S2 ). This provides further evidence that the transformation to extreme conditions (BCP) occurred within an overall switch to new conditions according to the mean chronology. We argue that the conversion of air masses formed at mid-and high-latitudes (Fig. 1 ) with those from the Mediterranean intensified in southern France at the onset of the YD (BCP), along the margin of the southward moving polar front; producing more intense cold season storms from both origins (enhanced δ 18 O* SW variability) and more frequent and/or more intense precipitation events (progressively increasing magnitude of negative Cave speleothem δ 18 O (‰ vs.