dataesr/scientific-paragraphs-categorization huggingface dataset:
The term refers to a machine learning technique which aims at pre-training a large neural network, generally on a "self-supervised" task, and then to fine tune the neural network on a so-called "downstream" task, different from the pre-training task. In our case, we built two models, each one with a downstream task. The first one classifies a sentence into three categories (politics, policy, or other), the second one labels each token ("off the record", or not). Self-supervised learning tries to take advantage of massive, unlabeled textual corpora by designing training objectives which don't need human annotation, yet capture crucial linguistic features. One simple way of generating a supervised dataset without any human annotation is to hide a small part of the data and try to guess it. In BERT, an algorithm developed by a team at Google  (Devlin et al.
Assessing the quality of the predictions In order to assess our model's performance, we compare its predictions to the one made by the expert on a smaller dataset. Using such a "test set" is a classic practice in machine learning. This dataset (sometimes also called "gold standard" or "ground truth") was never shown to the algorithm during the training phase. It is thus a good benchmark to assess the quality of the model's predictions. We use the F1-score as an evaluation metric. F1-score is the harmonic mean of the precision (the positive predictive value, which responds to the question "how reliable is the model when it predicts label A") and the recall (the sensitivity of the model, which answers the question "which proportion of label A is correctly identified by the model?") for a given label 14.
F1-score values range from 0 to 1, with 1 being a prediction that is identical to the consolidated test set. This measure has an important feature. Averaged over all labels, it gives a quality estimate of the prediction that is not affected by unbalanced datasets. This is especially important in our case, as the off-the-record labels are rare in our corpus. They represent only 2. 75% of the characters in the test set. In this case, using a basic accuracy measure would be misleading as a model predicting no off-the-record at all would yield a 97. 25% accuracy. 14 Formally,  = , where TP, FP, FN denote the number of true positive, false positive and false negative predictions.
The superiority of sequential transfer learning Let us recall that our primary goal is to build a model that can correctly identify either the label of a sentence ("policy vs. politics"), or the occurrence of a prompt signaling the use of unattributed discourse ("off-the-record"). More specifically, our hope is that an algorithm trained with the annotations made by the social scientist could equal, or even surpass the quality of the annotations made by the two other groups on the control dataset. Should this be the case, we could avoid outsourcing this activity to a third party, be it a handful of student research assistants or a crowd of microworkers, to annotate a dataset for us. Let us first compare the annotations made by the research assistants and the microworkers to those of the expert.
For the offthe-record, at least 50% of the annotations were required to reach such a plateau, which is equivalent to about 18 hours of annotation for the expert 19. The reason for such a difference between the tasks does not have much to do with their relative difficulty. As indicated above, both are complex, but the off-the-record task is probably easier than the policy/politics task. The surplus in time rather stems from the sparsity of the instances of "off-the-record" in the dataset (2. 75%). With such a low prevalence, a single annotator is forced to read thousands of texts if she wants to find enough examples to teach the algorithm a correct representation, but she eventually succeeds. This sample efficiency curve for the off-the-record task also shows that for annotation jobs that are simple (and potentially time consuming), it can be outsourced to other types of annotators.
There are other types of limitations to using language models. One has to do with the need for computational skills. To collect the texts, to clean and format them, but also to use stateof-the-art NLP methods requires a good knowledge in at least one computer programming language. These skills, nonetheless, disseminate rapidly and packaged solutions make the use of sequential transfer learning more democratic. The Transformers package democratizes the use of language models such as BERT  (Devlin et al. 2019)  or CamemBERT  (Martin et al. 2020). Moreover, using supervised learning algorithms requires a relatively user-friendly annotation interface. We used Doccano  (Nakayama et al. 2018) , an open-source annotation interface, which is easy to roll out and to maintain, even for people who don't have a developer background.
Conclusion The experiment demonstrates that using a supervised learning algorithm to fully annotate a textual database might not only be the cheapest, but also the best option available to researchers who want to analyze large scale text corpora. The time spent annotating the text for the training part is small enough that even a time-constrained academic can perform it. The scholar will, in addition, gain crucial insights into her material, and more often than not improve her coding scheme. But even if one were to outsource the annotating part to other humans, she would still gain from using such a tool, as the quality of the annotation is often increased, rather than decreased, by resorting to a model. Equipped with this technology, social scientists can yield more accurate results than the currently used alternatives.
generous comments made by the participants to the  Institute  for This research is supported by a grant from the  French National Research Agency (ANR) , " Investissements d'Avenir" (LabEx Ecodec/   ANR-11-LABX-0047 , and  ANR-19-P3IA-0001 ) and by the  Swedish Vetenskapsrådet  ( Project Mining  for Meaning, grant  2018-05170 ).
ACKNOWLEDGMENTS We thank  Dr. Takefumi Nakazawa  for inviting us to write this review article and  Dr. Shinichi Tatsumi  and an anonymous reviewer for insightful comments. This material is based upon work supported by the  JSPS KAKENHI  (grant No. 22K15188  to  Ryosuke Nakadai ), the  National Science Foundation  through the  Division of Environmental Biology  ( DEB 2015634  to  Akira Terui ), the  Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency of Japan  ( JPMEERF20214103  to  Naoto Shinohara ).
Health Barometer data and design weight The 2017 French Health Barometer survey used the RDD method, and included a respondent sample of 25,319 people aged 18 to 75 years, living in France, non-institutionalized, and speaking French. Interviews were conducted using computer-assisted telephone interviewing. Participation was anonymous and voluntary. In accordance with the guidelines of the French Data Protection Authority (Commission Nationale de l'Informatique et des Libertés, CNIL), all subject included in this study gave their informed verbal consent to participate before the telephone interview. The Health Barometer survey 2017 used an overlapping DF design of landline and cell phone numbers. For cell phones, the selected interviewee was the person who picked up the phone. For landline phones, the Kish selection method was used: it consists in listing all household members and then drawing randomly the person to interview among the eligible persons, with the same probability for each eligible person  [31].
For this purpose, we studied first the balance of the effective DF and of the counterfactual SF samples to the population reference margins for each of the calibration covariates. The reference margins of the population for calibration covariates were taken from the 2016 Labor Force Survey conducted by INSEE, restricted to the same agerange (18-75 years old). The representativeness of each sample (DF and SF) was then also assessed for several external covariates using the corresponding calibrated weights. These external covariates were socio-demographic characteristics (occupational status and socioprofessional group), and a health-related outcome (visits to a family physician in the preceding year), for which a gold-standard was available. These gold standards were taken from the 18-75 years old population in the 2016 Labor Force Survey for employment status and socio-professional group, and from National Health Insurance data for visits to a family physician in the preceding year.
For this purpose, control samples obtained using different data collection methodology would provide external benchmarks on key variables, which could be used in the weighting procedure, provided that the data collection method does not produce measurement biases  [50, 51]. Finally, in France, only landline numbers from agreeing owners are recorded in the telephone directory, in which only 25% of households are effectively present. As a consequence, letters can only be sent in advance to these households, although this approach substantially enhances the response rate  [52] [53] [54]. In addition, in France as in some other countries, a legal disposition allows any telephone owner to register in a file that prevents any commercial use of his number. Consequently, calls tend to be less accepted when the number is unknown: a substantial proportion of the cell phone owners never answers incoming calls from an unknown number, and in addition it is very easy to ban numbers with these devices.
for providing an environment where this research was started and CIREQ (Centre 1 and CIREQ in Montreal for their hospitality and acknowledges financial support from  JSPS KAKENHI  Grant No. 20K01565. Zuber acknowledges support by the  Agence nationale de la recherche  through the  Fair-ClimPop project  ( ANR-16-CE03-0001-01 ) and the  Investissements d'Avenir program  ( PGSE-ANR-17-EURE-01 ). The paper is part of the research activities at the  Centre for the Study of Equality, Social Organization and Performance (ESOP)  at the  Department of Economics at the University of Oslo.
Methods A systematic review of the literature was carried out by searching the PubMed and Google Scholar databases for the period from 2010 to 2022 following the PRISMA guidelines. The chosen starting year was 2010 because it was the year of publication of the study by Titus-Ernstoff et al. (14)  showing for the first time DES multigenerational effect in humans, although it only concerned somatic disorders. Only the study by Colborn published in 2004 (15) was added, as a tribute. Studies were identified using the following keywords: diethylstilbestrol, diethylstilbestrol and ASD, progestins, progestins and ASD, psychosis and endocrine disrupting compounds, estrogens and epigenetics. Publications that were not articles (e. g. conference abstracts) and publications that were not in English were excluded.
miscarriage or were at risk of miscarriage, but also to those in discomfort or even as a "morning after pill". The database of the families of the French patient association Hhorages-France allowed assessing the causal link between inutero exposure to synthetic hormones, particularly DES, and the occurrence of severe psychotic disorders after adolescence in exposed children. This database is based on the responses to a detailed questionnaire written by physicians and researchers and approved by the French national commission for data protection and freedom (CNIL)  (7). In 2016, we analyzed data on 1,182 pregnancies from 529 mothers from the Hhorages-France cohort (Table  1 ). We found that 603/720 children exposed to DES in-utero and 16 post-DES children (i. e. born after a previous exposed pregnancy but without in utero exposure) had psychiatric disorders, but none of the 180 children born before the pregnancy with DES treatment.
Estrogen impact on neurodevelopment: Epigenetic alterations that can be transmitted to the subsequent generations Data and families from the Hhorages-France cohort were used also to study the molecular basis of the causal link between in-utero exposure to synthetic estrogens and the appearance of psychotic disorders, such as schizophrenia and bipolar disorders  (26). From this cohort, 69 siblings (n=37 inutero exposed to DES: 12 boys and 25 girls; and n=32 unexposed: 12 boys and 20 girls) from 30 families were selected for DNA methylation analysis in peripheral blood samples. This analysis highlighted specific differences in DNA methylation at the ZFP57 and ADAMTS9 genes between exposed and unexposed participants that were c o r r e l a t e d w i t h p s y c h o t i c d i s o r d e r s , p a r t i c u l a r l y schizophrenia, depression and bipolar disorders.
Key findings on DES effects from this review -Psychiatric effects of estrogens and progestins in in-utero exposed children were analyzed using data from a French cohort. Comparisons of groups exposed to different synthetic progestins or estrogens (DES, EE) revealed that the frequency of psychiatric disorders (schizophrenia, bipolarity, eating disorders) is the same whatever the drug. -A study on the molecular mechanism of estrogen action in the brain of in-utero exposed children showed that psychosis was correlated with specific methylome modifications (hypermethylation) in two genes implicated in the neurodevelopment, thus highlighting an epigenetic mechanism. -Progestin may contribute to ASD development via an epigenetic mechanism (hypermethylation of the ERb gene promoter in the amygdala).
Acknowledgments This work could not continue without the daily ongoing support of the  Hhorages-France  families and board especially Mrs. Mauricette P. and  Yette B. Authors  warmly thank  Mr Simon Ball , Independent Autism/ADHD researcher, for his support and encouragements during the writing of the chapter concerning progestogen damages (9). Authors also thank  Elisabetta Eandermarcher  for corrections and relevant suggestions in the manuscript as well as  Professor Paul Yao  for invitation, encouragements and permission to reuse some of his team's results in Table 2.
Step 2: Scanning Scientific Database to Screen Papers Two leading electronic databases, WEB OF SCIENCE and SCIENCEDIRECT, are selected for articles review. On Web of Science, search strings "urban", "resilience", "indicator", "transport infrastructure" are picked as "Topic" items, with the conjunction "and", to conduct the search work in the first round. The term "transport network" and "transport system" are replaced by "transport infrastructure" in the second and third rounds of the search. The absent "Topic" items on SCIENCEDIRECT were changed by "Title, abstract or author-specified keywords". Therefore, the search work refers to search strings "urban transport infrastructure resilience indicator", "urban transport network resilience indicator" and "urban transport system resilience indicator".
Step 3: Selecting Suitable Papers After the first search in electronic databases, the selecting step aims to eliminate those that do not fit the research theme. The research result relying on keywords has a common problem: some found publications are not suitable for study objectives even though they have all keywords in their topics. For example, an unsuitable article, discussing impacts assessment of local resilience strategies to the urban transport network, could have "urban", "resilience", "indicator" and "transport network" as a topic keyword. Therefore, before the investigation of indication, this step helps select suitable articles in identifying the objective and goals of each screened article. Besides, resilience measurement and evaluation need to be remarked due to their important role in the resilience assessment.
Searching Result and Quantity Analysis The databases selected in WEB OF SCIENCE and SCIENCEDIRECT are before 6 pm, 08 February 2022. In WEB OF SCIENCE, 40 publications are screened, while in SCIENCEDI-RECT, 17 suitable articles are careened (see Table  2 ). Consequently, 29 articles will be discussed in relevancy analysis, since some articles of them repeat and two articles are not available on the online science website. "urban transport infrastructure resilience indicator" 6 "urban transport network resilience indicator" 3 "urban transport system resilience indicator" 8 For understanding the popularity of the study topic in the scientific discipline, this study apply to search for different combinations (conjunction "and") of keywords as "Topic" on WEB OF SCIENCE" and "Title, abstract or author-specified keywords" on SCIENCEDIRECT: -Category 1: combinations of "urban", "resilience" and "indicator" -Category 2: combinations of "transport infrastructure" and keywords of category 1 -Category 3: combinations of "transport network" and keywords of category 1 -Category 4: combinations of "transport system" and keywords of category 1 The result is shown through the state of the art (see Figure  3 ).
Results of Indicators' Analysis Da Mata Martins et al. [61]  assess the absorptive capacity of mobility resilience with the following indicator: possibility of changing the modes based on motorized vehicles to active modes in case of a given shock leading to a disruption in TIs. The indicator is measured with the maximum possible distances (MPD) possible by walking or cycling mode. The collected information is about the origin-destination (OD) datasets of the road network (internal system) and social risk sensitivity to motorized transport modes (based on population physical/health condition, along with terrain using the street gradients, road network topology). In the last step of his study, Da Mata Martins et al. [61]  map the distribution of resilient trips to apply the outcome to urban planning at a strategic level.
Search Method with Keywords This paper is based on a literature search online. However, the search work may be limited by the selected keywords. Based on an observation on resilience assessment framework, some studies on resilience indicator of TIs could not be screened in electronic databases with the keywords "indicator"  [50, 53, 97]. Some studies use terms or vocabularies similar to "indicators", such as index, indices, parameter, metrics, but they fit the definition of "indicator" in this study  [33, 98]. Some studies use variables to assess resilience, such as time, performance, damage, costs  [99] [100] [101]. These variables are indicators, but they cannot be found with the keyword "indicator" either. Therefore, the indicators identified by the search method with the keyword "indicator" are not all existing indicators.
Funding: The authors performed this work with funding from the UrbaRiskLab (URL) project (https://urbarisklab. org/fr/, accessed on 10 January 2022) and the RESIIST project (ANR-18-CE39-0018, https://researchgi. mines-albi. fr/display/14esist/RESIIST+Home (in French), accessed on 10 January 2022) that is jointly funded by the French National Research Agency (ANR) and the General Secretary of Defense and National Security (SGDSN). The authors acknowledge these organizations for their support that helped improve the paper.
Subjects This observational study enrolled 79 participants from 15 families who were registered in a database of the Bangkok home healthcare service (one family was randomly selected per district area) between 1 and 31 August 2021. Thirty-four cases were individuals who recovered from COVID-19 for at least 4 weeks before enrollment, while 45 cases were close contacts enrolled according to inclusion criteria (at least 1 asymptomatic case per family, with negative antigen test after COVID patients in family were diagnosed, living in the same accommodation following regulation of national health policy). The calculated sample size was 90 cases, based on the prevalence of asymptomatic COVID-19 in the Chinese population (4), with 80% power to detect asymptomatic infection. Peripheral blood (15 ml) was collected after written informed consent was obtained from each participant.
They should be able to share hopes, expectations, and dreams about the digital university and the capacity of change of the university that would allow for better experiences as students, teachers, and administrative staff. The steering group monitored and approved the quality and ethics of the expected project results against the progress indicators and the key question it sets at the beginning of the project. By the end of this project, it was expected that participants would have engaged in an imaginative process that they found relevant to the OpenU project, but also that they would feel their contributions had been received with openness and had contributed to the final outcome and that their results can feed into the downstream discussions and steps within OpenU. Personal data was limited to what was necessary for the purposes of processing the data: name, status in the university, and discipline.
In Phase 1, data was collected during focus groups and during pre-and post-session surveys. Through participant observation, text analysis was implemented in order to gather sentiment information. This technique allows the intentions and emotions of discourse to be understood and monitored, whether it is positive, negative, or neutral, and to be analyzed depending on certain factors. In order to maximize realism, classifications were only identified through participative observation, rather than being purposely triggered. Special attention was given to discourse-generated model practices and argumentative and/or legitimating elements. Furthermore, analyzing the argumentative, dramatizing, and evaluative statements, causal relations (cause-effect), and their link to responsibilities, problem dimensions, value implications, moral and aesthetic judgments, consequences, possible courses of action, and others, allowed building a case towards action generating schemes and frames, which were reflected by further outputs.
The dataset was checked for missing data and outliers. The responses obtained in the 28 questions were entered into the computer and started with a presentation of the data, a "flat sort". This was done in the form of a table (all the data were included, which is appropriate in the case of small numbers), or in the form of a graph, to give a synthetic view of the data, a general trend. After the simple presentation of the data, the hypotheses for analyzing and understanding the responses were discussed together with the ORIVE and the research team, question by question or criterion by criterion. This involved comparing the data with each other (from questionnaires and interviews) or with the existing review of literature. Some questions were cross-referenced to identify links. These cross-references and cross-sortings were presented in the form of a graph with the mention of a possible statistical link if the data were representative.
As the number of respondents was small, the results must be interpreted with caution and the percentages are given as an indication. In addition, due to rounding, some totals may be less than or greater than 100%. For the multiple-choice questions, the results were presented in a similar way to the single choice questions. As for open-ended questions, a content analysis was carried out where description and data analysis are presented together. An initial analysis of each question was carried out, reviewing consistency of responses, possible contradictions, and statements that directly or indirectly identify the respondent. This was followed by a cross-sectional analysis question by question. The report classically presents a question-by-question analysis, or more frequently a criterion-by-criterion analysis of all the interviews, illustrated by excerpts (with anonymity of the respondents).