dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements A first cut of this article was presented at a writing group in London with  Bernard Dionysius ,  Michael Meeuwis  and  Julia Ng  to whom we are most grateful for incisive comments. Thanks to  Marcel Broersma ,  Geof Bowker ,  Mercedes Bunz ,  Wendy Chun ,  Carolin Gerlitz ,  Alan Liu ,  Ella McPherson ,  Karin Raeymaeckers ,  Kriss Ravetto-Biagioli ,  Richard Rogers ,  Patrick Vonderau  and  Esther Weltevrede  for their correspondence, encouragement and reflections in the course of developing this work. We greatly benefitted from events at which different parts of this research were discussed and workshopped, including at the  University of Cambridge (March 2017 ),  Lancaster University  (April 2017), the  University of Bath  (September 2017),  King's College London  (October 2017),  University of Copenhagen and Lund University  (October 2017), the  University of Amsterdam  ( January 2018 ), the  University of Siegen  ( January 2018 ), the  University of Warwick  (May 2018),  Stockholm University  (September 2018), the  University of Edinburgh  (April 2019) and  Oslo Metropolitan University  (June 2019).
Data We constructed a longitudinal dataset at the district level in South Tyrol during 2000-2015 drawing on  Bosma and Sternberg (2014)  and  Fritsch et al. (2019) , which include various variables that affect regional entrepreneurship. The firm-level data on new business registered were taken from the Chamber of Commerce of South Tyrol and Istat (Italian Statistical Office). Our sample includes regional firms, which come from a single annual business registry cohort. To match the firms to the level of district from municipality level, we used detailed information on the firm's geographic location (5-digit post code) and a look-up table to match postcodes to the appropriate district. We used the confidential version of the data file, available through the Chamber of Commerce's restricted-access laboratory.
Once the data were classified as non-disclosure and were released to be used in this study, we chose to use the Chamber of Commerce data on new business registrations and new firm formation. Firstly, we covered all firms which are registered as new firms, firm exits including mergers and acquisitions as well as total businesses registered. This made our analysis compatible with other regions where similar indicators are used. Secondly, the data provide extensive geographic coverage. Firms in our sample were in 116 municipalities and eight aggregated districts. Once the business data was aggregated at a district level (Bezirksgemeinschaften), we matched various geographical, socioeconomic and cultural characteristics of districts from the Office of National Statistics (Istat) and the Chamber of Commerce.
We calculated two post-estimated predictive margins as suggested in Rising (  2012 ) for start-up rate and start-up density using bilingualism rate (Table  3 column  1 ) and bilingualism rate adjusted for public sector workers (Table  3 column 1 ). Sane estimation was performed with start-up density. We used the "margins" command in the statistical software STATA 15 to compute the standard errors of the means.
Background: The Chinese government proposed the "XIAO BING BU CHU CUN, DA BING BU CHU XIAN" initiative in 2016, which states the rate of health care service provided by county hospitals should reach 90% of overall health care service provision. The prerequisite for achieving this goal is that citizens should be able to access county hospitals' services conveniently and impartially. However, little research has been done on the actual levels of the spatial accessibility of citizens to county hospitals in Western China. Therefore, we aimed to measure the spatial accessibility to county hospitals for county residents and to identify any regional disparities in Shaanxi Province in Western China. Methods: We implemented a novel methodinvolving utilizing navigation data from the AutoNavi web mapping system (knows as Gaode map in Chinese)to assess the time and distance from villages and neighborhoods to the county hospitals.
The most common method to measure time and distance between points is to use road networks in the GIS package together with information on the applicable speed limit criteria on those road networks to measure travel time  [7, 9, 23]. Some limitations of this approach include that the traditional census is conducted at intervals and therefore includes hysteresis, that errors can occur from cutting up the map, and that using road network maps can lead to rough estimates. Therefore, this study aimed to measure spatial accessibility to county hospitals and any disparities in accessibility for county residents in 73 administrative counties in Shaanxi Province of Western China. We first used the navigation data of online digital maps to assess the time and distance from residents to county hospitals in Shaanxi Province.
Data collection scheme In order to measure the spatial accessibility to health resources, three types of data were basically needed: geographical distribution of the population, the geographical location of hospitals, and the time and distance between residents and the hospitals. Therefore, we collected data in three steps. First, considering the uneven distribution of the population, we used the geographical location of the villages and neighborhoods to identify the population distribution. Here, two strategies were adopted in our study: i) For villages and neighborhoods with a village clinic, we selected the coordinates of the village clinics to represent the population distribution since the village clinics should be in an area with relatively high concentration of the village population to cover the population of the village to the greatest extent.
ii) For villages and neighborhoods whose village clinic we could not acquire or that have multiple village clinics, we selected the default coordinates provided by the web mapping navigation service provider. This coordinate usually defaults to the location of the village or neighborhood office, which is usually located in a populated area. Second, we obtained the names of the county hospitals from the Health Commission of Shaanxi Province, and then we directly used the names of the hospitals to get their geographical locations from the web map. Third, the time and distance between each village and neighborhood to the county hospitals were collected from the navigation results provided bythe web mapping navigation service. We chose the fastest route, but not the highway route (because China's highway import and export are usually set around the county), to get the time and distance from villages and neighborhoods to the local county hospital by using the real-time navigation data of the AutoNavi map in the driving mode.
Data collection method To perform the data collection, first, we obtained the names of the village clinics and county hospitals in overall Shaanxi Province from the Shaanxi Provincial Health Statistics Annual Report in 2017, which was provided by the Health Commission of Shaanxi Province. In addition, we also obtained the names of the village and neighborhood committees in overall Shaanxi Province from the website of the National Bureau of Statistics  [25]. Second, we used the geocoding interface of AutoNavi map to collect the coordinates of all the villages and neighborhoods and county hospitals. The requests through the API for the geocoding of the AutoNavi map were conducted by using a web crawler (a web data extraction tool) in the Python 3. 6 program  [26]. The URL of this geocoding interface can be found in the footnote below.
Availability of data and materials The data were collected from AutoNavi map, a Chinese web map and navigation service provider. This web map provides an open application programming interface for freely using data after registering as a developer. The application programming interface can be found here: (https://lbs. amap. com/api/webservice/guide/api/georegeo#geo) & (https://lbs. amap. com/api/ webservice/guide/api/direction#driving).
Data Processing All multivariate statistical approaches were performed by using the R software  [43]. For LDA, the "lda" function of the MASS package was used. For the PCA analyses, the "PCA" function of the FactoMineR package was used. The hierarchical clustering was performed by using the "hclust" function on Euclidian distances. The ward method was selected as advised by precedent studies (Charlton the quest of provenance). Because of the iron reduction, causing a global enrichment of the elements remaining in the slag, the raw amounts of the elements cannot be used directly. They have to be normalized. Different ways of normalization are possible  [12, 44, 45] , but we chose to perform the log ratio one, the most used in literature  [1, 19]. Trace element contents are normalized by a log ratio approach given by the equation: X E = ln([E]) - 1 N N k=1 ln([E k ]).
9 for C1 and C2a, 3 for C2a and 4. 5 for the other clusters). Such a low ratio is relatively unusual and was rarely reported in our former studies on trace slag and SI trace elements where Nd/Sm is mostly around 4. 5. Moreover, the rare production areas from our database with a similar Nd/Sm are not chemically compatible with C1 and C2 when one considers all the other trace elements (not shown here). Figure  7  shows the PCA results on the normalized data (Xij) from the complete set of samples (i. e. , slag from Nozières, Allogny and Aubois areas and all the artefacts coming from the different locations in the cathedral). The projection on PC1 and PC2 highlights different clusters. A hierarchical agglomerative clustering (HAC) with 5 clusters, using Ward's method, allowed us to extract the samples corresponding to these different clusters.
It is interesting to note that none of the considered production areas fits with these clusters which seem to have a very different trace element composition, with higher ratios of Yb, Tb, Gd, Eu, Sm, Y compared to the other elements as illustrated in Figure  9. It is also worth noting that, for example, the Nd/Sm ratios for these two clusters are sharply different than those of the other clusters (about 1. 9 for C1 and C2a, 3 for C2a and 4. 5 for the other clusters). Such a low ratio is relatively unusual and was rarely reported in our former studies on trace slag and SI trace elements where Nd/Sm is mostly around 4. 5. Moreover, the rare production areas from our database with a similar Nd/Sm are not chemically compatible with C1 and C2 when one considers all the other trace elements (not shown here).
Acknowledgments: The authors are grateful to  P. Ponsot , Architecte en des Monuments Historiques and curator  J. -P. Blin  for letting them access to the  Cathedral of Bourges  and for the authorization of sampling. The authors would particularly like to thank  Jean-Marie Bordeloup ,  Christophe Dunikowski  and  Emmanuèle Gautier-Costard  for their field experience and their support during the archaeological prospections.
• European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). 27 In agreement with Giesinger et al,  28  we excluded from data analyses one item measuring financial difficulties and two items measuring the quality of life. Based on the 27 remaining items, the questionnaire includes five functioning scales: (a) physical; (b) role; (c) emotional; (d) cognitive; and (e) social. It also measures symptomatology with three scales (Nausea and Vomiting, Fatigue, Pain) as well as with five separate items (Dyspnea, Insomnia, Appetite Loss, Constipation, and Diarrhea). All scores are transformed into a 0-100 scale, with a higher score representing a better health. For conceptual matters, we have distinguished physical and mental health as we have done for the cross-sectional and longitudinal study.
Empathy for pain task The participant and the confederate were told that the aim was to evaluate the partner's pain sensitivity and that this measure had to be done individually because the mere presence of another person might bias pain sensations. They were then told that during a conversation based on a video conferencing program (Skype) they will watch their partner undergoing the painful task and that every 10 s they will have to report on a visual analogue scale from "no pain" to "worst imaginable" the intensity of the pain they thought the partner experienced. The confederate was always first chosen to experience the "cold pressure task" (i. e. , when the person's hand is immersed in a tank filled with ice water). Participants were told that they will subsequently switch in order to undergo the same experience.
After these instructions, the participant and the confederate were asked to go back to their respective cubicle to start the task. Importantly, during the Skype conversation, participants watched a video of the confederate previously videotaped during an actual cold pressure task. Participants viewed the recorded videotape displayed on a computer monitor through the video conferencing program camera. At the end of the video, participants had to complete the perspective taking questions and the situational affective responses questionnaire.
Physiological measures Participants' heart rate (HR) was measured with a Polar Watch RS800CX using electrodes contained in a chest belt, in wireless mode. Afterwards, the Polar ProTrainer5 software was used to extract signal information. Polar watches have been used in previous studies and show satisfactory validity and reliability in measuring HR and Heart Rate Variability compared to other mobile or more classic ECG measurements devices (e. g. (Gamelin et al. , 2008; Kingsley et al. , 2005; Nunan et al. , 2008; Quintana et al. , 2012; Radespiel-Tröger et al. , 2003; Weippert et al. , 2010) ). We measured participants' HR in order to 2 Six confederates were needed to reach the sample size because they all worked for a limited period of time as research assistants in the Department of Psychology.
Statistical methods Statistical analyses were performed using the SPSS software package Version 21 (SPSS Inc. , Chicago, IL). Univariate ANOVA was used to test the effect of group on closeness measures. Repeated Measures were used to compare empathic concern and personal distress, self and other perspective, and HR during Baseline 1, 2 and empathy induction. Pearson correlations and partial correlations investigated the association between variables. The significance level was set at p <. 05.
, 2015; Cheng et al. , 2010; Pillai Riddell & Craig, 2007). We also showed that higher ratings of other's pain was associated with reporting more dispositional personal distress, supporting previous findings that pain-catastrophizing individuals (i. e. , presenting an exaggerated negative orientation towards noxious stimuli 3 Heart Rate Variability (HRV) was also measured at Baseline 1, Baseline 2 and during the empathy for pain task but the data are not reported in the article. Indeed, there was neither effect of group on HRV measures (LF, HF, ratio LF/HF, and VLF) nor significant correlation between HRV after the closeness induction (Baseline 2) or during the empathy for pain task (controlling for HRV at Baseline 1) and the other variables (closeness, distress and empathic concern responses, imagine self, imagine other or intensity rating).
Data, Sample, and Statistics We obtain data on national R&D investment between 1996 and 2013 from United Nations Educational, Scientific and Cultural Organization (UNESCO) Institute for Statistics. 9 The full sample has 52 countries on six continents, including almost all large economies of the world. Among them, 15 are classified as countries with weak-FTR languages. As mentioned earlier, we construct a large set of country-level economic, legal, cultural, and religious variables. Table  1  presents summary statistics. Business R&D and total R&D are on average 1% and 1. 5% of GDP. 29% of the country-year observations are of weak FTR. Summary statistics of the rest are comparable to related studies in the literature. We compute time-series average of Business R&D/GDP for each country. We tabulate the averages in Appendix I and plot them in Figure  2.
Data, Sample, and Statistics We obtain firm accounting and financial data from Thomson Reuters Worldscope database. The Worldscope database offers fundamental data on the world's leading public companies and covers more than 95% of global market capitalization. We restrict our sample to the primary quotes (i. e. , non-cross-listings), and exclude financial firms (ICB code 8000) and utility firms (ICB code 7000). We further delete firm-years with missing data for any of the firm-specific and country-specific control variables used in Equation (  9 ). This results in a final sample consisting of 34 countries, 31,650 firms and 215,877 observations for the 1985-2013 period. We start our sample from 1985 because Worldscope coverage is relatively limited prior to 1985. We winsorize all firm-level variables at the 1st and 99th percentiles to reduce the influence of extreme outliers.
Innovation Output We obtain country-level patent-based innovation output measure from the Organization for Economic Cooperation and Development (OECD), namely Pat OECD , defined as the number of triadic patents reported by the OECD. The OECD patent data are particularly useful for crosscountry comparison of patents because the data cover triadic patents that are filed at three major patent offices in different countries and thus are much less subject to the home bias that domestic inventors tend to file only in their home country. After merging these patent measures with the control variables used in Table  2 , we have data for 34 countries over the period 1996-2013. Table  14  reports the regression result of patent on Weak-FTR. In each regression we control for the full set of control variables used in Table  2  and continent fixed effects.
However, spatial factors and urban form such as inadequate access conditions, inefficient use of space, high ground occupation, and limited public space are not included in the definition, even though they arguably constitute fundamental urban deficiencies. The work presented in this paper seeks to contribute to the understanding of informal settlements through the analysis of urban form. of the surveyed areas. Since orthophotos cannot fully map the intricate path systems typical of informal settlements, these were mapped by walking along the boundaries of every block in each settlement with a handheld GPS device (smartphone) equipped with SW Maps (http://swmaps. softwel. com. np/#), an app that records GPS traces. The GPS traces and the high-resolution orthophotos were then automatically scaled in GIS and subsequently exported in a design software application.
Acknowledgments: We would like to thank the municipality and local authorities in the five settlements studied in this paper for permitting the spatial surveys as well as the members of the communities of each settlement who participated in the field interviews. Finally, we would like to thank the following residents who facilitated the field work:  Mario Mugano Xikumbane ,  Ali Muhammed Muhina ,  Ally Hassan Ally ,  Daniel Futwax ,  John Saturday Bwambale ,  David Esenga ,  Kyeyune Daniel ,  Vero Razafinirina ,  Ravaonirina Andrine ,  Rasoanirina Zoeline , and  Rabevaza Selestin.
In a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automatically detect sexist content but also to identify if a message with a sexist content is really sexist or is a story of sexism experienced by a woman. We propose: (1) a new characterization of sexist content inspired by speech acts theory and discourse analysis studies, (2) the first French dataset annotated for sexism detection, and (3) a set of deep learning experiments trained on top of a combination of several tweet's vectorial representations (word embeddings, linguistic features, and various generalization strategies). Our results are encouraging and constitute a first step towards offensive content moderation.
This is presented in Section 3. Our guiding hypothesis is that indirect acts establish a distancing effect with the reported content and are thus less committal on behalf of the addressee  (Giannakidou and Mari, 2021). Our take on the issue is language-driven: reported speech is indirect, and it does not discursively involve a call on the addressee to endorse the content of the act. (2) The first French dataset of about 12, 000 tweets annotated for sexism detection according to this new characterization 4. Data and manual annotation are described in Section 4. (3) A set of experiments to detect sexist content in three configurations: binary classification (sexist content vs. non-sexist), three classes (reporting content vs. non-reporting vs. non-sexist), and a cascade classifier (first sexist content and then reporting).
, 2018) : discredit, stereotype, objectification, sexual harassment, threat of violence, dominance and derailing. Most participants used SVM models and ensemble of classifiers for both tasks with features such as n-grams and opinions  (Fersini et al. , 2018b). These datasets have also been used in the Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter shared task at SemEval 2019. Best results were obtained with an SVM model using sentence embeddings as features  (Indurthi et al. , 2019). There are also a few notable neural network techniques. Jha and Mamidi (2017)  employ an LSTM model to classify messages as: benevolent, hostile and non-sexist. Zhang and Luo (2018)  implement two deep neural network models (CNN + Gated Recurrent Unit layer and CNN + modified CNN layers for feature extraction) in order to classify social media texts as racist, sexist, or non-hateful.
Karlekar and Bansal (2018)  use a single-label CNN-LSTM model with character-level embeddings to classify three forms of sexual harassment: commenting, ogling/staring, and touching/groping. Sharifirad et al. (2018)  focus on diverse forms of sexist harassment (indirect, information threat, sexual, physical) using LSTM and CNN on augmented dataset obtained via ConceptNet is-a relationships and Wikidata. Finally,  (Parikh et al. , 2019)  consider messages of sexism experienced by women in the "Everyday Sexism Project" web site and classify them according to 23 non mutually exclusive categories using LSTM, CNN, CNN-LSTM and BERT models trained on top of several distributional representations (character, subwords, words and sentence) along with additional linguistic features. In this paper, we propose different deep learning architectures to detect reporting of sexist acts and, more importantly, distinguishing them from real sexist messages.
Data and Annotation Our corpus is new and contains French tweets collected between October 2017 and May 2018. In order to collect sexist and non sexist tweets, we followed  Anzovino et al. (2018)  approach using: (i) a set of representative keywords: femme, fille (woman, girl), enceinte (pregnant), some activities (cuisine (cooking), football,. ), insults, etc. , (ii) the names of women/men potentially victims or guilty of sexism (mainly politicians), (iii) specific hashtags to collect stories of sexism experiences 6  : #balancetonporc, #sexisme, #sexiste, #SexismeOrdinaire, #EnsembleContreLeSexisme, #payetashnek, #payetontaf, etc. The tweets collected with these hashtags may contain reported sexist acts towards both men and women. Thus, we collected around 205, 000 tweets, among which about 70, 000 contain the specific hashtags.