‘Teaching’ the path towards university: understanding student access through storied-futures and meritocratic grand narratives

The notion that the stories of our lives shape dispositions towards imagined futures is another lens through which university underrepresentation should be viewed. A storied lens attends to how futures, like university attendance, are storied during childhood to the extent that some youth imagine, and therefore plan, these futures as natural progression. Such was the case for study participants, indicating that childhood stories contain answers for mitigating social reproduction connected to parental education and cultural forces within the home. Examining social mobility through the underexplored storied-futures grand narrative presents unique solutions that lay hidden within the dominant meritocratic grand narrative.     

体医融合项目的风险治理:一种新的面向与选择——从非典到新型冠状肺炎谈起

体医融合和非医疗健康干预可以有效促进疾病管理与健康服务模式的形成,但体医融合项目治理初期仍然受到理念融合、体医融资、部门协同等因素的影响。该文以病毒、人体免疫与体育运动的体医融合关系视角,依据共生理论对体医融合项目的风险治理逻辑进行了阐述。采用文献资料法、数理统计法等对体医融合项目风险治理的相关个案进行了整理,并基于NVIVO11.0质性分析软件对体医融合个案进行识别分析,构建了体医融合项目风险治理的影响因素评价指标体系,包括13个一级指标30个二级指标。最后提出了体医融合项目风险治理的路径选择(1)将体医融合理念提升至构建公共卫生保障体系战略的高度;(2)落地变现体医融合项目购买方案;(3)打造政府精准购买体医融合项目区块链。     

Adherence to 24-hour movement guidelines among Portuguese preschool children: the prestyle study

ABSTRACT

Purpose

The 24-Hour Movement Guidelines (24 hMG) provide specific recommendations, including physical activity (PA), screen-time and sleep-duration, which preschoolers should achieve for a healthy day (24 h). The aim of the current study was to analyse preschooler’s adherence with the 24-Hour Movement Guidelines.     

灰色预测法在情报研究中的应用研究综述

[目的/意义] 灰色预测法可有效处理情报研究中广泛存在的小样本数据，通过对灰色预测法在情报研究中的应用情况进行梳理，总结其在应用过程中存在的不足，为灰色预测法在情报研究中的进一步应用提供参考。[方法/过程] 通过综述情报研究中涉及灰色预测法的相关文献，从数据选取、模型构建和解决的问题等方面对情报研究中灰色预测法的应用进行概述，总结当前情报研究中灰色预测法的应用所存在的问题，并提出改进建议。[结果/结论] 在方法应用上，已有研究主要采用数列灰预测，且模型集中在单变量灰色预测模型，根据预测对象不同，灰色预测法已经在包括期刊分析、图书馆运行管理、热点主题分析及科研机构评价方面得到了很好的应用，未来可根据预测对象特点及研究目标尝试不同的灰色预测方法，扩宽灰色预测法在其他方面的情报研究问题中的应用。     

指向武术核心素养的中小学武术有效教学策略与方法设计

为了有效促进学生武术核心素养的培育,针对我国武术教学中存在套路内容“繁难”、教法单一、重“形”轻“击”、重知识灌输,轻自主性培养、重教师的“教”,轻学生的“学”“学生不喜欢武术课”等一系列问题,提出了有效促进武术认知、武术运动能力、武术人格培育的原则,基于此原则,对“化难为易”策略、“化浅为深”策略、“化无趣为有趣”策略、“化技术为实践”策略、“知新温故”等策略进行了具体探索性设计,并对上述策略的具体实施方法进行了阐述,为中小学武术教学改革提供现实指导。     

Where do you come from,where do you go? Assessing skills gaps and labour market outcomes for young adults with different immigration backgrounds

Using PISA and PIAAC data from twelve OECD countries, we examine the gap in cognitive skills among immigrants and natives and evaluate how those differences have evolved over time. We also consider how demographics, family background and school quality explain the development of cognitive skills of young people with immigrant backgrounds. The results show, first, that some convergence in skills occurs between second-generation immigrants and natives over time. Second, demographics, family background and school quality variables all contribute to the achievement gaps across different groups.     

Quality flaw prediction in Spanish Wikipedia: A case of study with verifiability flaws

In this work, we present the first quality flaw prediction study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove). Based on the underlying characteristics of each flaw, different state-of-the-art approaches were evaluated. For articles not citing any references, a well-established rule-based approach was evaluated and interesting findings show that some of them suffer from Refimprove flaw instead. Likewise, for articles that need additional citations for verification, the well-known PU learning and one-class classification approaches were evaluated. Besides, new methods were compared and a new feature was also proposed to model this latter flaw. The results showed that new methods such as under-bagged decision trees with sum or majority voting rules, biased-SVM, and centroid-based balanced SVM, perform best in comparison with the ones previously published.     

图书情报学领域学科交叉及其国家差异的动态分析

[目的/意义]领域知识的跨学科交叉研究能够打破学科间的壁垒,有助于发现重大科学问题的解决方案。[研究设计/方法]基于图书情报学领域文献题录信息构建轻量级领域知识图谱,从中提取学科信息、国家信息、时间信息及其关联,采用时间与空间相结合的多维度分析方法,对学科交叉的演化进程以及国家差异进行跟踪与分析。[结论/发现]图书情报学领域内学科交叉现象日渐显著,各国家的学科交叉程度与倾向存在差异,一些目前尚不突出的交叉学科方向值得关注。[创新/价值]采用多维度视角分析学科交叉现象,相关结论可以为国家科技战略制定及学科发展规划提供有益参考。     

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).