12,668 research outputs found

    An exploration of the language within Ofsted reports and their influence on primary school performance in mathematics: a mixed methods critical discourse analysis

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    This thesis contributes to the understanding of the language of Ofsted reports, their similarity to one another and associations between different terms used within ‘areas for improvement’ sections and subsequent outcomes for pupils. The research responds to concerns from serving headteachers that Ofsted reports are overly similar, do not capture the unique story of their school, and are unhelpful for improvement. In seeking to answer ‘how similar are Ofsted reports’ the study uses two tools, a plagiarism detection software (Turnitin) and a discourse analysis tool (NVivo) to identify trends within and across a large corpus of reports. The approach is based on critical discourse analysis (Van Dijk, 2009; Fairclough, 1989) but shaped in the form of practitioner enquiry seeking power in the form of impact on pupils and practitioners, rather than a more traditional, sociological application of the method. The research found that in 2017, primary school section 5 Ofsted reports had more than half of their content exactly duplicated within other primary school inspection reports published that same year. Discourse analysis showed the quality assurance process overrode variables such as inspector designation, gender, or team size, leading to three distinct patterns of duplication: block duplication, self-referencing, and template writing. The most unique part of a report was found to be the ‘area for improvement’ section, which was tracked to externally verified outcomes for pupils using terms linked to ‘mathematics’. Those required to improve mathematics in their areas for improvement improved progress and attainment in mathematics significantly more than national rates. These findings indicate that there was a positive correlation between the inspection reporting process and a beneficial impact on pupil outcomes in mathematics, and that the significant similarity of one report to another had no bearing on the usefulness of the report for school improvement purposes within this corpus

    An Experimental Study on Sentiment Classification of Moroccan dialect texts in the web

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    With the rapid growth of the use of social media websites, obtaining the users' feedback automatically became a crucial task to evaluate their tendencies and behaviors online. Despite this great availability of information, and the increasing number of Arabic users only few research has managed to treat Arabic dialects. The purpose of this paper is to study the opinion and emotion expressed in real Moroccan texts precisely in the YouTube comments using some well-known and commonly used methods for sentiment analysis. In this paper, we present our work of Moroccan dialect comments classification using Machine Learning (ML) models and based on our collected and manually annotated YouTube Moroccan dialect dataset. By employing many text preprocessing and data representation techniques we aim to compare our classification results utilizing the most commonly used supervised classifiers: k-nearest neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and deep learning (DL) classifiers such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LTSM). Experiments were performed using both raw and preprocessed data to show the importance of the preprocessing. In fact, the experimental results prove that DL models have a better performance for Moroccan Dialect than classical approaches and we achieved an accuracy of 90%.Comment: 13 pages, 5 tables, 2 figure

    Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning

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    The spread of rumors along with breaking events seriously hinders the truth in the era of social media. Previous studies reveal that due to the lack of annotated resources, rumors presented in minority languages are hard to be detected. Furthermore, the unforeseen breaking events not involved in yesterday's news exacerbate the scarcity of data resources. In this work, we propose a novel zero-shot framework based on prompt learning to detect rumors falling in different domains or presented in different languages. More specifically, we firstly represent rumor circulated on social media as diverse propagation threads, then design a hierarchical prompt encoding mechanism to learn language-agnostic contextual representations for both prompts and rumor data. To further enhance domain adaptation, we model the domain-invariant structural features from the propagation threads, to incorporate structural position representations of influential community response. In addition, a new virtual response augmentation method is used to improve model training. Extensive experiments conducted on three real-world datasets demonstrate that our proposed model achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.Comment: AAAI 202

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    The development of the Kent coalfield 1896-1946

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    One of the unique features of the Kent Coalfield is that it is entirely concealed by newer rocks. The existence of a coalfield under southern England, being a direct link between those of South Wales, Somerset and Bristol in the west and the Ruhr, Belgium. and northern France in the east, was predicted by the geologist R. A. C. Godwin-Austen as early as 1856. It was, however, only the rapid increase in demand for Britain's coal in the last quarter of the nineteenth century that made it worth considering testing this hypothesis. The first boring was made in the years 1886-90, and although it discovered coal, this did not in itself prove the existence of a viable coalfield. This could be done only by incurring the heavy cost of boring systematically over a wide area. As the financial returns from such an undertaking were uncertain, it was not surprising that in the early years, around the turn of the century, a dominant role was played by speculators, who were able to induce numerous small investors to risk some of their savings in the expectation of high profits. As minerals in Britain were privately owned, the early pioneer companies not only had to meet the cost of the exploratory borines, but also, if they were not to see the benefit of their work accrue to others, lease beforehand the right to mine coal from local landowners in as much of the surrounding area as possible. This policy was pursued most vigorously by Arthur Burr, a Surrey land specula tor, who raised capital by creating the Kent Coal Conoessions Ltd. and then floating a series of companies allied to it. Burr's enterprise would probably have been. successful had it not been for the water problems encountered at depth in -v- the coalfield. As a result, the Concessions group found itself in control of most of the coalfield, but without the necessary capital to sink and adequately equip its 01ffi collieries. By 1910, however, the discovery of iron ore deposits in east Kent, coupled with the fact that Kent coal was excellent for coking purposes, began to attract the large steel firms of Bolckow, Vaughan Ltd. and Dorman, Long & Co. Ltd. in to the area. The First World War intervened, however, to delay their plans, and to provide an extended lease of life to the Concessions group, which, by the summer of 1914, was facing financial collapse. By the time Dorman, Lone & Co, in alliance with Weetman Pearson (Lord Cowdray), had acquired control over the greater part of the coalfield from the Concessions group, not only was the country's coal industry declining, but so was its steel industry, which suffered an even more severe rate of contraction during the inter-war years. As a result, Pearson and Dorman Long Ltd. was forced to concentrate just on coal production, and this in turn was hampered not only by the water problems, but also by labour shortages and the schemes introduced by the government in 1930 to restrict the country's coal output, in an attempt to maintain prices and revenue in the industry. Nevertheless, production did show a substantial increase between 1927 and 1935, after which it declined as miners left the coalfield to return to their former districts, where employment opportunities were improving in the late thirties. Supporting roles were played in the inter-war years by Richard Tilden Smith, a share underwriter turned industrialist with long standing interests in the coalfield, who acquired one of the Concessions group's two collieries, and by the Powell Duffryn Steam Coal Co. Ltd., which through subsidiary companies, took over the only colliery to be developed by a pioneer company outside the Concessions group. The impossibility of Kent coal, because of its nature, ever gaining more than token access to the more lucrative household market, and then the failure of the local steel industry to materialise meant that the -vi- companies had to develop alternative outlets for their growing outputs. Although nearness to industrial markets in the south-east of England did confer certain advantages were poor consolation for the hoped for developments of either the early pioneers or the later industrialists. Instead of the expected profits, the companies mostly incurred losses, and only the company acquired by Powell Duffryn ever paid a dividend to its shareholders in the years before nationalisation. From the point of view of the Kent miners, the shortage of labour in the coalfield, particularly in the years 1914-20 and 1927-35, was to an important extent responsible for their being amongst the highest paid in the industry. At the same time the more favourable employment opportunities prevailing in Kent compared with other mining districts enabled the Kent Nine Workers Association to develop into a well organised union, which on the whole was able to look after the interests of its members fairly successfully. Throughout the period 1896 to 1946 the Kent Coalfield existed very much at the margin of the British coal industry. Its failure to develop substantially along the lines envisaged by either the early pioneers or by the later industrialists meant that its importance in national terms always remained small

    MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter

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    To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Towards a more just refuge regime: quotas, markets and a fair share

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    The international refugee regime is beset by two problems: Responsibility for refuge falls disproportionately on a few states and many owed refuge do not get it. In this work, I explore remedies to these problems. One is a quota distribution wherein states are distributed responsibilities via allotment. Another is a marketized quota system wherein states are free to buy and sell their allotments with others. I explore these in three parts. In Part 1, I develop the prime principles upon which a just regime is built and with which alternatives can be adjudicated. The first and most important principle – ‘Justice for Refugees’ – stipulates that a just regime provides refuge for all who have a basic interest in it. The second principle – ‘Justice for States’ – stipulates that a just distribution of refuge responsibilities among states is one that is capacity considerate. In Part 2, I take up several vexing questions regarding the distribution of refuge responsibilities among states in a collective effort. First, what is a state’s ‘fair share’? The answer requires the determination of some logic – some metric – with which a distribution is determined. I argue that one popular method in the political theory literature – a GDP-based distribution – is normatively unsatisfactory. In its place, I posit several alternative metrics that are more attuned with the principles of justice but absent in the political theory literature: GDP adjusted for Purchasing Power Parity and the Human Development Index. I offer an exploration of both these. Second, are states required to ‘take up the slack’ left by defaulting peers? Here, I argue that duties of help remain intact in cases of partial compliance among states in the refuge regime, but that political concerns may require that such duties be applied with caution. I submit that a market instrument offers one practical solution to this problem, as well as other advantages. In Part 3, I take aim at marketization and grapple with its many pitfalls: That marketization is commodifying, that it is corrupting, and that it offers little advantage in providing quality protection for refugees. In addition to these, I apply a framework of moral markets developed by Debra Satz. I argue that a refuge market may satisfy Justice Among States, but that it is violative of the refugees’ welfare interest in remaining free of degrading and discriminatory treatment
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