Gender on the box: Self-reflexivity as a starting point

This self-reflexive activity acts as an introduction to how we talk about and express gender identity, as well as the assumptions we may have about gender identity norms and expression. The activity illuminates student’s subconscious behaviors and understandings of gender, pushing them to sit self-reflexively with their own understandings of gender as an identity, expression, binary, and potential locus of shame/freedom.

Courses: Introduction to Women and Gender Studies, Intercultural Communication, Media Studies, Gender and Communication, Performance Studies

Objectives: Designed to introduce students to their own understandings and embodiment of gender, this activity asks students to be honest about their preconceived notions regarding gender that they bring with them into the classroom. The activity utilizes predesigned components that test students’ subconscious knowledge of the gender binary. This is a one-time activity that can be conducted in one 50- or 75-minute class period.     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

儿童青少年运动员的营养需要

1. 儿童青少年运动员应该食用多样化的食物，其中，蛋白质应占膳食总能量的12％-15％，碳水化合物至少占55％，而脂肪至多占30％。日常生活中如何吃才能满足这些要求呢？膳食指南宝塔提供了一条可行之道。2.有些营养素对热爱运动的儿童青少年运动员非常重要，要保证摄入充足，这些营养素是碳水化合物（包括纤维素）、维生素B6、维生素D、铁、钙、镁、锌、铬。科学合理、包罗各类食物的平衡膳食，对青少年运动员最大程度地全面摄取各种营养素，满足生长发育和运动训练的需要，是非常必要的。3.鼓励儿童青少年运动员在运动训练前、中、后进行补液，以避免脱水的发生。4.如果膳食合理平衡，就没必要额外补充营养品。     

Eine generische Komponente zur rechnergestützten Nutzung von Aufbauorganisationen

Zusammenfassung.   Die klassische Unterscheidung zwischen Aufbau- und Ablauforganisation eines Unternehmens deutet auf eine getrennte rechnergestützte Realisierung dieser beiden fundamentalen Komponenten einer Organisation hin. Dieser Beitrag unterstützt diese These, verweist aber gleichzeitig auf die integrierende Bedeutung einer rechnergestützten Aufbauorganisationsverwaltung, welche unterschiedliche Realisierungsformen der Ablauforganisation (unter anderem workflow-management und eCommerce) bedient. Die generelle Konzeption und Umsetzung einer solchen Aufbauorganisationsverwaltung wird in diesem Beitrag vorgestellt. Eingegangen am 14. Februar 2000 / Angenommen am 31. Mai 2000     

A method for transcribing signed and spoken language

Transcribing sign communication used simultaneously with spoken English presents investigators with a unique problem: the singular quality of the bimodal communicative interactions cannot be accurately depicted using accepted conventions for recording either spoken or signed language samples. This article proposes guidelines for transcribing such data. The need for guidelines arose during an earlier study of the language development of a hearing child of deaf parents. To meet the immediate needs of that study, rules and conventions from previous studies were combined with newly generated ones, resulting in the guidelines proposed in this article. The guidelines can be applied to data in which intermodal linguistic influence is suspected.