387,631 research outputs found

    Second Language Teaching Research: The Priming Methods Perspective

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    The aim of this paper is to examine the use of priming method in second language (L2) research. The study adopts a descriptive approach for analysis of data. Our intention is to subject this method (priming) of L2 research to analysis, and see how it is applied. Our data-base is drawn from a lot of illustrative texts. Priming method of L2 research strengthens learners’ abilities to understand particular linguistic activities with ease. People are more likely to describe an agentive than non-agentive events when explaining a particular causal event, especially the one they have recently encountered. That is to say agentive events are more accurately described by people than non-agentive ones. In everyday conversation, priming clearly influences our linguistic choices through the use of agentive and non-agentive event descriptions respectively. We discover that priming involves visual linguistic activities, and also involves diversifying of utterances for clarity purpose. It is further discovered that agentive and non-agentive causal event descriptions can be primed linguistically.

    Risk evaluations and condom use decisions of homeless youth: a multi-level qualitative investigation.

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    BackgroundHomeless youth are at higher risk for sexually transmitted infections and unwanted pregnancy than non-homeless youth. However, little is known about how they evaluate risk within the context of their sexual relationships. It is important to understand homeless youths' condom use decisions in light of their sexual relationships because condom use decisions are influenced by relationship dynamics in addition to individual attitudes and event circumstances. It is also important to understand how relationship level factors, sexual event circumstances, and individual characteristics compare and intersect.MethodsTo explore these issues, we conducted semi-structured interviews with 37 homeless youth in Los Angeles County in 2011 concerning their recent sexual relationships and analyzed the data using systematic methods of team-based qualitative data analysis.ResultsWe identified themes of risk-related evaluations and decisions at the relationship/partner, event, and individual level. We also identified three different risk profiles that emerged from analyzing how different levels of risk intersected across individual respondents. The three profiles included 1) Risk Takers, who consistently engage in risk and have low concern about consequences of risk behavior, 2) Risk Avoiders, who consistently show high concern about protection and consistently avoid risk, and 3) Risk Reactors, those who are inconsistent in their concerns about risk and protection and mainly take risks in reaction to relationship and event circumstances.ConclusionsInterventions targeting homeless youth should reflect multiple levels of risk behavior and evaluation in order to address the diversity of risk profiles. Relationship/partner-, event-, and individual-level factors are all important but have different levels of importance for different homeless youth. Interventions should be tailored to address the most important factor contributing to homeless youth reproductive needs

    GCE AS and A level subject criteria for law

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    Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)

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    Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. On the other hand, their opaqueness has led to a surge of interest in explainable systems. In this work, we emphasize the importance of model explanation in various forms such as visual pointing and textual justification. The lack of data with justification annotations is one of the bottlenecks of generating multimodal explanations. Thus, we propose two large-scale datasets with annotations that visually and textually justify a classification decision for various activities, i.e. ACT-X, and for question answering, i.e. VQA-X. We also introduce a multimodal methodology for generating visual and textual explanations simultaneously. We quantitatively show that training with the textual explanations not only yields better textual justification models, but also models that better localize the evidence that support their decision.Comment: arXiv admin note: text overlap with arXiv:1612.0475

    Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks

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    Numerous HR applications are centered around resumes and job descriptions. While they can benefit from advancements in NLP, particularly large language models, their real-world adoption faces challenges due to absence of comprehensive benchmarks for various HR tasks, and lack of smaller models with competitive capabilities. In this paper, we aim to bridge this gap by introducing the Resume-Job Description Benchmark (RJDB). We meticulously craft this benchmark to cater to a wide array of HR tasks, including matching and explaining resumes to job descriptions, extracting skills and experiences from resumes, and editing resumes. To create this benchmark, we propose to distill domain-specific knowledge from a large language model (LLM). We rely on a curated skill-occupation graph to ensure diversity and provide context for LLMs generation. Our benchmark includes over 50 thousand triples of job descriptions, matched resumes and unmatched resumes. Using RJDB, we train multiple smaller student models. Our experiments reveal that the student models achieve near/better performance than the teacher model (GPT-4), affirming the effectiveness of the benchmark. Additionally, we explore the utility of RJDB on out-of-distribution data for skill extraction and resume-job description matching, in zero-shot and weak supervision manner. We release our datasets and code to foster further research and industry applications

    Specifying computer-supported collaboration scripts

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    Collaboration scripts are activity programs which aim to foster collaborative learning by structuring interaction between learners. Computer-supported collaboration scripts generally suffer from the problem of being restrained to a specific learning platform and learning context. A standardization of collaboration scripts first requires a specification of collaboration scripts that integrates multiple perspectives from computer science, education and psychology. So far, only few and limited attempts at such specifications have been made. This paper aims to consolidate and expand these approaches in light of recent findings and to propose a generic framework for the specification of collaboration scripts. The framework enables a description of collaboration scripts using a small number of components (participants, activities, roles, resources and groups) and mechanisms (task distribution, group formation and sequencing)
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