There has been a recent wave of work assessing the fairness of machine learning models in general, and more specifically, on natural language processing (NLP) models built using machine learning techniques. While much work has highlighted biases embedded in stateof-the-art language models, and more recent efforts have focused on how to debias, research assessing the fairness and performance of biased/debiased models on downstream prediction tasks has been limited. Moreover, most prior work has emphasized bias along a single dimension such as gender or race. In this work, we benchmark multiple NLP models with regards to their fairness and predictive performance across a variety of NLP tasks. In particular, we assess intersectional bias -fairness across multiple demographic dimensions. The results show that while current debiasing strategies fare well in terms of the fairnessaccuracy trade-off (generally preserving predictive power in debiased models), they are unable to effectively alleviate bias in downstream tasks. Furthermore, this bias is often amplified across demographic dimensions. We conclude with implications for future NLP debiasing research.
Problem Statement: Traditional forms of assessment such as essays and end of term examinations are still widely used in higher education in Ireland as the sole assessment method. These forms of assessment, while they may be valid and reliable approaches for collecting evidence of the acquisition of theoretical knowledge, rarely afford students the opportunity to apply knowledge to key professional scenarios. In the context of teacher education, if the aim is to develop teacher competence beyond the mere possession of technical skills then appropriate pedagogic and curriculum interventions need to be developed, implemented and evaluated.Purpose of Study: This paper argues that reflection and experiential learning should be infused through effective assessment strategies and embedded in the training and formation of trainee-teacher attributes. The authors draw on their experience as lecturers and module/course designers for an 'Assessment' module within a teacher-training degree programme in a School of Education in the Republic of Ireland.Methods: This paper presents the findings of a 4-year study, which adopted a multi-methods approach. The research was conducted using both numerical and qualitative tools. A primary focus of the research used student reflection to generate relevant data suitable for analysis and this was then triangulated with module evaluations and numerical performance data. The paper describes the research that used
This review focuses on the similarities and differences between prolonged grief disorder (PGD) and post-traumatic stress disorder (PTSD). It highlights how a PTSD-related understanding aids the investigation and clinical management of PGD. Grief has long been understood as a natural response to bereavement, as serious psychological and physiological stress has been regarded as a potential outcome of extreme or traumatic stress. PTSD was first included in DSM-III in 1980. In the mid-1980s, the first systematic investigation began into whether there is an extreme or pathological form of mourning. Meanwhile, there is much research literature on complicated, traumatic, or prolonged grief This literature is reviewed in this article, with the following questions: Is it possible to distinguish normal from non-normal grief? Which clinical presentation does PGD have-and how does this compare with PTSD? Finally, diagnostic, preventive, and therapeutic approaches and existing tools are presented.
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