dataesr/scientific-paragraphs-categorization huggingface dataset:
J 22 Figure 10. 2210 Figure 10. Temporal requirements in the graphical interface of t. vect. createRoutes. py. All the code written for this work (including both cleaning data and geoprocessing routines) has been released as Open Source software (license GNU GPL v. 3). It can be freely downloaded from https://github. com/annalisapg/maritimeTraffic/tree/v. 1. 0 (accessed on 3 March 2021) and it is published in Zenodo (https://doi. org/10. 5281/zenodo. 4420051, accessed on 3 March 2021).
The major assumption of the salience-based analyses is that the more governments talk about DI, the more relevant it is. In order to evaluate how muchand in what mannerdifferent government bodies refer to DI, I examine a variety of sources, from more general documents (governing party government programmes, prime minister annual Discours sur l'Etat de la Nation) to more specific ones (Prime Minister speeches dedicated to the EU and extended records of parliamentary debates in the National Assembly). The data analysed in this report consist of (1) government programmes, (2) inauguration speeches/Prime Minister vote of confidence speeches and the following debates, (3) President speeches in the European Parliament and the following debates and (4) records of parliamentary committees. See Appendix 1 for an overview of the documents included in the analysis.
Methods Administratively, Tidore Kepulauan municipality is part of North Maluku province. Its area scatters in three different islands, namely Tidore Island, Mare Island, and Halmahera Island. However, the study particularly assessed Tidore Island, where the Kiematubu volcano is situated (Figure  2 ). There are 31 villages' lies on Tidore Island, which is included in four sub districts. Tidore Kepulauan municipality data in 2020 was employed along with spatial data of topographical maps and high-resolution satellite imagery data. Some parameters to access social vulnerability consist of demographic condition, education and knowledge, health, institution, and social insurance  [2, 11]. Detailed variables of each parameter are shown in Table  1. Assessment of social vulnerability used the village as the unit of analysis.
People response is analyzed with respect to sentiments and misinformation and malicious information detection. This paper proposes CMTA, a multilingual tweet analysis and information (misinformation) detection method for understanding both the negative and positive sides of social media during COVID-19 pandemic. CMTA uses Multilingual BERT, trained on 104 multiple languages to derive features from tweets and 1D convolution for finding the correlation between data of hidden states. It also uses a dense layer for linear transformation on contextual embeddings to provide inferential points. Our work helps in providing better results in finding the proximity of being fake. We used manually annotated multilingual COVID-19 related tweets for training deep neural network model in order to detect and identify the type of misinformation present in tweets belonging to different language groups.
Training Dataset In order to train and test our misinformation detection model, we collected the training data from an online fact-checker website called Poynter (Poynter Institute, 2020). Poynter have a specific COVID-19 related misinformation detection program named 'CoronaVirusFacts/DatosCoronaVirus Alliance Database 1  '. This database contains thousands of labelled social media information such as news, posts, claims, articles about COVID-19 which were manually verified and annotated by human volunteers(fact-checkers) from all around the globe. The database gathers all the misinformation related to topics such as COVID-19 cure, detection, the effect on animals, foods, travel, government policies, crime, lockdown. The misinformation dataset was available in 2 languages-'English' and 'Spanish'.
Since we were training a multilingual BERT model, we crawled through the content of all 2 websites using Beautifulsoup 2  , a Python library for scraping information from web pages. We scrape 8471 English language false news/information belonging to nine major classes namely, 'False', 'Partially false', 'Misleading', 'No evidence', 'Four Pinocchios', 'Incorrect', 'Three Pinocchios', 'Two Pinocchios' and 'Mostly False'. For each article we gath-   6  gives a clear understanding of our training dataset and showcase some misinformation articles present in our training dataset. Column 1 shows the reformed label assigned by us, column 2 shows the original label assigned by the factchecker, column 3 gives a misinformation example associated with the label present in column 2, and column 4 provides a reasoning given by the factchecker behind assigning a particular label (column 2) to the misinformation (column 3).
Inference Dataset Once we finished training our multilingual tweet misinformation detection model we aimed to use it for predicting and analysing the misinformation spread across all over the social media platforms in multiple languages. In order to do so, we collected around 2,137,106 multilingual tweets consisting of tweets belonging to eight major languages, namely-'English', 'Spanish', 'Indonesian', 'French', 'Japanese', 'Thai', 'Hindi' and 'German'. We used an ongoing dataset of tweets IDs associated with the novel coronavirus COVID-19  (Chen et al. , 2020). Started on January 28, 2020, the current version of dataset contains 212,978,935 tweets divided into groups based on their publishing month. The dataset was collected using multilingual COVID-19 related keywords and contains tweets in more than 30 languages.
g. tweets) into three misinformation classes: 'false', 'partly false' and 'misleading'. Text tokenization Given a multilingual textual dataset consisting of sentences, CMTA uses the BERT multilingual tokeniser to generate tokens that BERT's embedding layer will further process. CMTA uses MBERT 11  to extract contextual features, namely word and sentence embedding vectors, from text data 12. In the subsequent CMTA phases that use NLP models, these vectors are used as feature inputs with several advantages. (M)BERT embeddings are word representations that are dynamically informed by the words around them, meaning that the same word's embeddings will change in (M)BERT depending on its related words within two different sentences. For the non-expert reader, the tokenization process is based on a WordPiece model.
Dense Layer Pooling In the case of CMTA, the tokenisation is more complex because it is done for sentences written in different languages. Therefore, it relies on the MBERT model that has been trained for this purpose. Feature Extraction Phase is intended to exploit the information of hidden-layers produced due to applying BERT to the tokenisation phase result. The objective is to get individual vectors for each token and convert them into a single vector representation of the whole sentence. For each token of our input, we have 13 separate vectors, each of length 768. Thus, to get the individual vectors, it is necessary to combine some of the layer vectors. The challenge is to determine which layer or combination of layers provides the best representation.
Dataset Proprocessing In data preprocessing, we performed cleaning and structuring of the training and inference dataset. The collected dataset contained lots of unnecessary noises and components such as emojis, symbols, numeric values, hyperlinks to websites and username mentions which were needed to be removed. Since our dataset was multilingual, we had to be very careful while preprocessing as we did not wanted to lose any valuable information. We used simple regular expressions to remove URLs, special characters or symbols, blank rows, re-tweets, user mentions but we did not removed the hashtags from the data. As hashtags might contain useful information. For example in the sentence-'Wear mask to protect yourself from #COVID-19 #corona', only '#' symbol was removed during the preprocessing(e.
g. 'Wear mask to protect yourself from COVID19 corona'). We removed stop words using NLTK 16 , a Python library for natural language processing. NLTK supports multiple languages except few languages such as Hindi and Thai in our case. For preprocessing Hindi dataset we used CLTK(Classical Lan-276 guage Toolkit) 17 which supports Hindi stop words. For removing Thai stop words from Thai tweets, we used PyThaiNLP  (Wannaphong Phatthiyaphaibun, 2016). The emojis were removed using their unicodes. For training our model we divided the dataset into training, validation and testing dataset in the ratio of 80%/10%10% respectively. The final count for train, validation and test dataset was 7,602, 950, 950.
Model Setup and Training Training Setting We fine-tuned the Sequence Classifier from HuggingFace based on the parameters as specified in  (Devlin et al. , 2018). Thus, we set a batch size of 32, learning rate 1e-4, with Adam Weight Decay as the optimizer. We run the model for training for 10 epochs. Then, we save the model weights of the transformer. These will be helpful for the further training. Hyperparameters' Setting Table  3  lists every hyperparameter for training and testing our model. All the calculations and selection of hyperparmaters are done based on tests and for the best output from the model. After performing several iterations on distinct sets of hyper-parameters, based on the analysis of the model's performance, we adopted the one showing promising results on our dataset.
Results assessment This section discusses the performance our multilingual model over the test data. On the test dataset, our model was able to achieve an accuracy(%) of 82. 17 and F 1 (%) of 82. 54. The precision and recall reported by the model were 82. 07 and 82. 30 respectively. few examples from the test dataset along with their actual label. As we shown in the table, the model prediction in case of entry number '1', '2', '3' and '4' our model was able to predict the correct the label. But in case of entry number '5' the label predicted by our model was 'False' whereas the actual label is 'Misleading'. If we would look at the misinformation at the entry number '5' which is a Spanish text-'El medicamento contra piojos sirve como tratamiento contra Covid-19. ' and who's English translation would be-".
Multilingual Misinformation Analysis In this section, we provide a detailed analysis misinformation distribution across the multilingual tweets. We used our trained multilingual model to predict and categorize the misinformation type present in tweets. We conducted our sequential misinformation analysis on a collection of over 2 million multilingual tweets. Our survey studied and analyzed the distribution of COVID-19 misinformation across eight major languages, (i. e. 'English', 'Spanish', 'Indonesian', 'French', 'Japanese', 'Thai', 'Hindi' and 'German') for five months (i. e. February, March, April, May and June). Figure  4  shows the month-wise distribution of misinformation types for each language. Thai have the least of all. Following more specific observation made with respect to the languages: • English: The misinformation distribution for English data, indicates that there is a majority of False tweets during the five months, whereas the distribution of Misleading labelled data is slightly less than as compared to False labelled data.
Conclusion and Future Work In this paper, we presented a BERT based multilingual model for analysing COVID-19 related multilingual tweets. We performed a detailed systematic survey for detecting disinformation spread on the social media platform-Twitter. We were able to detect misinformation distribution across eight major languages and presented a quantified magnitude of misinformation distributed across different languages in last 5 months. We also demonstrated that our single multilingual CMTA framework performed significantly well as compared to the monolingual misinformation detection models. We strongly believe that our model can help in filtration of misinformation and factual data present in multiple languages during the pandemic. In future, we aim at collecting more annotated training data and performing analysis of a larger multilingual dataset to gain deeper understanding.
Indonesian: In our distribution for Indonesian tweets approximately 10% of tweets are labelled as Misleading and in contrary there is a large distribution of False labelled tweets. The frequency of Hindi tweets is low in the dataset used in our experiment. Yet, our model can predict or label Hindi tweets. Tweets in Hindi have low numbers of Misleading tweets, whereas the Partially False tweets class has a great frequency. False labelled tweets are slightly low compared to Partially False tweets in this dialect.
• Indonesian: In our distribution for Indonesian tweets approximately 10% of tweets are labelled as Misleading and in contrary there is a large distribution of False labelled tweets. The frequency of Hindi tweets is low in the dataset used in our experiment. Yet, our model can predict or label Hindi tweets. Tweets in Hindi have low numbers of Misleading tweets, whereas the Partially False tweets class has a great frequency. False labelled tweets are slightly low compared to Partially False tweets in this dialect. Approximately 34% of the data in Indonesian dialect is labelled as Partially False through- out the five months. • French: Figure4 shows the misinformation distribution across all of the five months in the French tweets. The largest majority of the tweets were classified as False misinforma- tion.
More specifically, we apply our empirical approach to the implementation of two antagonistic views regarding the role of the correlation between circumstances and effort. Firstly, our baseline model follows  Barry (2005) , which entails incorporating the observed effort variables (including preferences and talent) into a model of school performance outcomes without any prior modelling of effort. For a given dataset and choice of variables, this model yields the maximum possible contribution of effort variables toward inequality in school performance; thereby minimising the measure of inequality of opportunity stemming from the contribution of variables deemed beyond pupil's control. Secondly, we investigate whether the correlation between effort and circumstances can mitigate the role of effort found in the baseline model.
We use data from a unique survey undertaken among students attending secular schools and registered-aided Islamic schools 2 in Bangladesh. Detailed data on family and social background characteristics as well as school and teachers for more than 300 schools are available. Besides, the dataset provides an accurate picture of the degree of dispersion in several outcomes of interest like mathematics and English test scores as well as pupils' efforts. The dataset allows us to adopt an encompassing view of overall effort, which includes dimensions of preferences for leisure and eagerness to study, as well as social skills at class, and talent for mathematics and English. We find that the contribution of overall effort comes first in explaining within-school variations in test scores, whereas the contribution of circumstances (i.
This result underlines that in the case of rural Bangladesh effort accounts for about one third of total explained inequality and so cannot be omitted. These results do not depend much on the choice between a fixed-and a random-effect estimation strategy; they are also robust to purging the correlation between effort and circumstances as advocated by  Roemer (1998). The contribution of effort does not decrease by more than 5 percentage points in mostly all cases. The rest of the paper proceeds as follows. Section 2 provides a summary of the previous literature. Section 3 explains the rural Bangladesh context and describes key features of the dataset and the chosen performance outcomes, effort indicators, and circumstances. Section 4 describes our new measurement method for the baseline model.
Social background Our vector of parental circumstances, thoroughly described in Table  A2 , is based on empirical studies in inequality of opportunity literature  (Ramos & Van de Gaer, 2016; Jusot & Tubeuf, 2013). We measure family social background using dummies of educational attainment for fathers and mothers measuring whether they completed only primary education; did some secondary education; completed up to secondary education; did some tertiary education. The omitted category is incomplete primary education (or less). We also include indicators of household living conditions as further circumstances potentially associated with student 10 We thank an anonymous referee for pointing out that self-discipline constitutes a socio-emotional skill. 11 This way of proceeding spares us the need to implement ordered multinomial models that do not rely on the proportional odds assumption, which is violated in our dataset whenever we model the effort indicators as a function of family circumstances.
Estimation phase We implement and expand on the proposal by  Jusot et al. (2013) , exploiting the special features of our dataset, chiefly the rich information on schools attended. Since we want to gauge the empirical relevance of the different views as to how the correlation between efforts and circumstances should affect the magnitude of legitimate and illegitimate sources of inequality in education in rural Bangladesh, we model the variation of indicators of student achievement in mathematics (A m ) and in English language (A l ), as functions of a vector of students' demographic and religion variables (D), a vector of efforts, talents and preferences in mathematics (E m ) only included in the equation for mathematics achievement (respectively vector (E l ) of efforts in English), a vector of their social background circumstances (C) including parental effort in their offspring's education, a vector of school characteristics (Q), and a vector of teacher's characteristics in mathematics (T m ) only included in the equation for mathematics achievement (and a respective vector T l for the English achievement equation).
Alternatively, peer effects might be more important than parental traits  (Harris, 1998 ) (though we controlled for the former in our models). A third option involves a degree of a student's individual agency which cannot be attributed to any particular environmental factor. In the absence of additional observable factors in our dataset, this would be a possible interpretative consequence by default. But we should not rule out the potential role of some contextual features specific to rural Bangladesh such as the relative low level of inequality in some monetary indicators of well-being  (Osmani & Sen, 2011) , and the relative cultural homogeneity of the population (as exemplified by the prominence of Hanafi Sunni Islam). 27.
Conclusion We used a unique dataset on secondary schools' inputs and outcomes in rural Bangladesh to evaluate the importance of effort relative to circumstances in student performance. We found that various measures of pupil's effort, preferences, and talent, jointly explain between one third and two fifths of the explained variance in mathematics and English. This result undermines the idea that educational achievement should be considered as a circumstance in the assessment of inequality of opportunity in income acquisition (or related adult advantages) in every context. In the absence of data to measure effort, the role of circumstances could therefore be biased and overestimated. The remaining explained part is clearly beyond children's control, whether they be parental background variables, gender, age or school quality.
Acknowledgments We are grateful to  Paul Hufe , the editor and two anonymous referees for helpful comments on an earlier version of this work. This research work was presented and received comments from colleagues at the  HEAL seminar series (University of Lancaster ), the 6th and 7th meetings of the ECINEQ respectively in Luxembourg and  New York City , the 14th LAGV workshop in Aix-en-Provence, the PEGNet 2017 conference in Zurich, the 13th International Conference of the  WEAI in Santiago , the  School of Economics at Nagoya University (Japan) , the  Applied Economics workshop at the Institute for Economics Studies at Keio University (Japan) , the  Health Economics  seminars at  Tinbergen Institute at Erasmus University , and Health Economics seminars at the  University of Manchester.
Industry Need Popular job search engines, including Hitmarker and Rektjobs, are often used by students and job seekers to gauge and apply for esports careers. They are considered two of the most popular search engines for esports careers, although numerous others exist. These were selected as starting points to determine the level of industry demand for the roles that the working group have identified and what the employer expectations may have be for certain positions. Though, being English language job sites, a limitation is that they will not likely be representative of global demand, and more so of demand in English-speaking countries.
Positioning of Company in Esports Industry Adjacent Support Some Initiatives Focused In total, there were 1,533 roles in the dataset 3 that were listed on these platforms on 25th June 2021. The working group identified 347 companies advertising roles. The jobs were classified into the schema presented in Section 5 by two raters who deductively coded the data. Agreement between the raters reached 93% for the jobs and 98% for companies, with disagreements resolved through discussion with the rest of the working group. This analysis was then extended to identify those companies which could be considered esports companies and to what extent they were positioned in the sector-classifying them as: 'focused', where the company was largely focused on esports, with it representing the core of their business model; 'some initiatives', where the company had strong involvement in esports as a clear part of its business but did not operate exclusively in the esports sector; 'supporting', where the company produced products, including platforms and tools, that actively supported the esports sector, or exclusively offered services that were tailored to the esports sector but that might not be considered an esports initiative in itself (e.
Following the screening process, this meant that subsequent analysis focused on the remaining 984 job adverts from 183 companies. Of which the top 30 recruiters by number of posts represented 69% of the available roles. A summary of these is provided in Table  1. Demand according to the domains identified by the working group in Section 5. 1 is summarised in the bar chart in Figure  5  above. Figure  6  on the following page gives an impression of the job titles in the dataset. The development and maintenance of esports commands a considerable share of the available career pathways in the esports industry. It is worth noting that these jobs are varied in terms of discipline, with an apparent demand for animators and software engineers. The management of an esport game title was the next most frequently type of role advertised.
It could be seen that career opportunities in esports vary greatly across the esports ecosystem. There are currently careers ranging from the management and business sector to: coaching; wellness, nutrition, and performance management; content creation and media production; game design; computer science; and more. Esports represents a multidisciplinary area of study which extends to the variances in esports careers across the ecosystem. However, it is interesting to note that a surprisingly considerable proportion of the roles being advertised in this field require computing skills, particularly in the areas such as data analytics, software engineering, and user experience design. Aside from the limitations of the data presented in this report, it is also important to consider that the current landscape of careers in esports is not an accurate depiction of the total space as there are potential opportunities that may exist in coming years that have not yet been created.