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Literature reviews (LRs) play an important role in the development of domain knowledge in all fields. Yet, we observe a lack of insights into the activities with which LRs actually develop knowledge. To address this important gap, we (1) derive knowledge building activities from the extant literature on LRs, (2) suggest a knowledge-based typology of LRs that complements existing typologies, and (3) apply the suggested typology in an empirical study that explores how LRs with different goals...
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The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. A qualitative research approach, leveraging the use of literature review as a research design and approach was used and effectively facilitated the realization of the study purpose....
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This article proposes a three-step methodological framework called computational grounded theory, which combines expert human knowledge and hermeneutic skills with the processing power and pattern recognition of computers, producing a more methodologically rigorous but interpretive approach to content analysis. The first, pattern detection step, involves inductive computational exploration of text, using techniques such as unsupervised machine learning and word scores to help researchers to...
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Background Automated approaches to improve the efficiency of systematic reviews are greatly needed. When testing any of these approaches, the criterion standard of comparison (gold standard) is usually human reviewers. Yet, human reviewers make errors in inclusion and exclusion of references. Objectives To determine citation false inclusion and false exclusion rates during abstract screening by pairs of independent reviewers. These rates can help in designing, testing and implementing...
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Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely...
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Abstract Understanding how climate change adaptation is integrated into existing policy sectors and organizations is critical to ensure timely and effective climate actions across multiple levels and scales. Studying climate change adaptation policy has become increasingly difficult, particularly given the increasing volume of potentially relevant data available, the validity of existing methods handling large volumes of data, and comprehensiveness of assessing processes of...
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Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require “more” from RE: a more powerful RE system...
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Abstract Coastal areas have been affected by hazards such as floods and storms due to the impact of climate change. As coastal systems continue to become more socially and environmentally complex, the damage these hazards cause is expected to increase and intensify. To reduce such negative impacts, vulnerable coastal areas and their associated risks must be identified and assessed. In this study, we assessed the flooding risk to coastal areas of South Korea using multiple machine...