931 research outputs found

    Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model

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    Sentiment and emotion analysis is a common classification task aimed at enhancing the benefit and comfort of consumers of a product. However, the data obtained often lacks balance between each class or aspect to be analyzed, commonly known as an imbalanced dataset. Imbalanced datasets are frequently challenging in machine learning tasks, particularly text datasets. Our research tackles imbalanced datasets using two techniques, namely SMOTE and Augmentation. In the SMOTE technique, text datasets need to undergo numerical representation using TF-IDF. The classification model employed is the IndoBERT model. Both oversampling techniques can address data imbalance by generating synthetic and new data. The newly created dataset enhances the classification model's performance. With the Augmentation technique, the classification model's performance improves by up to 20%, with accuracy reaching 78%, precision at 85%, recall at 82%, and an F1-score of 83%. On the other hand, using the SMOTE technique, the evaluation results achieve the best values between the two techniques, enhancing the model's accuracy to a high 82% with precision at 87%, recall at 85%, and an F1-score of 86%

    An examination of the verbal behaviour of intergroup discrimination

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    This thesis examined relationships between psychological flexibility, psychological inflexibility, prejudicial attitudes, and dehumanization across three cross-sectional studies with an additional proposed experimental study. Psychological flexibility refers to mindful attention to the present moment, willing acceptance of private experiences, and engaging in behaviours congruent with one’s freely chosen values. Inflexibility, on the other hand, indicates a tendency to suppress unwanted thoughts and emotions, entanglement with one’s thoughts, and rigid behavioural patterns. Study 1 found limited correlations between inflexibility and sexism, racism, homonegativity, and dehumanization. Study 2 demonstrated more consistent positive associations between inflexibility and prejudice. And Study 3 controlled for right-wing authoritarianism and social dominance orientation, finding inflexibility predicted hostile sexism and racism beyond these factors. While showing some relationships, particularly with sexism and racism, psychological inflexibility did not consistently correlate with varied prejudices across studies. The proposed randomized controlled trial aims to evaluate an Acceptance and Commitment Therapy intervention to reduce sexism through enhanced psychological flexibility. Overall, findings provide mixed support for the utility of flexibility-based skills in addressing complex societal prejudices. Research should continue examining flexibility integrated with socio-cultural approaches to promote equity

    Analisis sentimen artikel berita pemilu berbasis metode klasifikasi

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    Penyaluran informasi berupa berita online begitu masif di tengah masyarakat luas, sehingga sulit membedakan berupa berita haox ataupun berita positif. Sehingga dibutuhkan klasifikasi mengenai sentimen publik tentang pelaksanaan pemilu dengan menggunakan data artikel berita media mainstream yang menggunakan data uji 1064 dataset. Metode yang digunakan adalah pada penelitian ini adalah algoritma naive bayes, algoritma random forest, dan algoritma support vektor machine. Model uji coba menggunakan smote dimana hasil performa yang dilakukan oleh algortima yang digunakan dengan menggunakan smote dan tidak menggunakan smote, dimana random forest menghasilkan akurasi 91,88%, sedangkan tampa menggunakan smote support vektor machine menghasilkan akurasi 92,05%

    Expert Ignorance:The Law and Politics of Rule of Law Reform

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    Bias and Fairness in Large Language Models: A Survey

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    Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touch our social sphere. Despite this success, these models can learn, perpetuate, and amplify harmful social biases. In this paper, we present a comprehensive survey of bias evaluation and mitigation techniques for LLMs. We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing, defining distinct facets of harm and introducing several desiderata to operationalize fairness for LLMs. We then unify the literature by proposing three intuitive taxonomies, two for bias evaluation, namely metrics and datasets, and one for mitigation. Our first taxonomy of metrics for bias evaluation disambiguates the relationship between metrics and evaluation datasets, and organizes metrics by the different levels at which they operate in a model: embeddings, probabilities, and generated text. Our second taxonomy of datasets for bias evaluation categorizes datasets by their structure as counterfactual inputs or prompts, and identifies the targeted harms and social groups; we also release a consolidation of publicly-available datasets for improved access. Our third taxonomy of techniques for bias mitigation classifies methods by their intervention during pre-processing, in-training, intra-processing, and post-processing, with granular subcategories that elucidate research trends. Finally, we identify open problems and challenges for future work. Synthesizing a wide range of recent research, we aim to provide a clear guide of the existing literature that empowers researchers and practitioners to better understand and prevent the propagation of bias in LLMs

    Handbook Transdisciplinary Learning

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    What is transdisciplinarity - and what are its methods? How does a living lab work? What is the purpose of citizen science, student-organized teaching and cooperative education? This handbook unpacks key terms and concepts to describe the range of transdisciplinary learning in the context of academic education. Transdisciplinary learning turns out to be a comprehensive innovation process in response to the major global challenges such as climate change, urbanization or migration. A reference work for students, lecturers, scientists, and anyone wanting to understand the profound changes in higher education

    Measuring abortion stigma in Australia and Aotearoa New Zealand: the development, adaptation, and validation of multiple individual-level instruments

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    The stigmatisation of abortion is a pervasive influence on the prohibiting, threatening, and undermining of quality abortion care. In Australia and Aotearoa New Zealand (ANZ) abortion stigma impacts abortion care quality, including the experiences of accessing, providing, and supporting abortion. Although there are qualitative reports of how abortion stigma is experienced in ANZ, quantitative details are scant. This thesis aimed to address gaps in quantifying abortion stigma in ANZ. To understand how to best measure abortion stigma in ANZ, we conducted a systematic review of approaches quantifying abortion stigma globally. No instrument measuring abortion stigma in ANZ was found. The Individual Level Abortion Stigma scale (ILAS) and Abortion Providers Stigma Scale – Revised (APSS-R) have the most robust psychometric properties according to rigorous guidelines for evaluating measurement properties. The ILAS and APSS-R measure individual level abortion stigma. Through qualitative inquiry, the ILAS and APSS-R were reviewed for use in ANZ and their appropriateness for measuring stigmatisation of people, groups, and organisations supporting abortion care in ANZ. Four instruments measuring individual-level abortion stigma in ANZ were generated for: A) people who have had an abortion; B) people who provide abortion related care; C) people who publicly support abortion; and, D) groups/organisations supporting and/or providing abortion care. The four ANZ instruments were revised by representatives of the relevant end-user groups. Through an online survey, the instruments have been psychometrically tested for Australia demonstrating validity and reliability. These instruments can improve our understanding of abortion stigma and the evaluation of interventions addressing abortion stigma. Future co-designed research should explore the role of research in stigmatising abortion, revise the instruments for specific subgroups, and explore short form versions of the instruments
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