Most ‘traditional’ research into second language development tends to focus on groups of learners that are representative for larger populations based on statistics and on the Gaussian distribution. This allows us to make generalizations about second language development that describe general tendencies and relationships. In contrast, studies inspired by Complex Dynamic Systems Theory (CDST) tend to focus on the development over time of using single or multiple case studies. This allows us to closely follow and describe the process of development of individual learners. The two approaches seem to cover contradictory perspectives on L2 development. Group studies allow for generalizations, but cannot say anything about the individuals in the group. Case studies show us the development of individual learners, but these observations cannot be generalized beyond the individual learner. In this presentation, I will discuss the contrastive dimensions of research into L2 development and consider solutions to solve the apparent incommensurability of the two approaches. I will make use of the construct of ergodicity as described in Lowie and Verspoor (2019), and will discuss a new (and still ongoing) study that focuses on learners’ use of mobile devices to support language learning (Peng, Jager & Lowie, forthcoming), where the group meets the individual.
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1. 口译员的职业生涯体会
2. 英译实践中的典型中式表达
3. 中式英文的识别与探讨

战略语言教师学科能力提升计划之亚非语青年教师学术工作坊第3场暨学术咖啡第21场
时 间：2021年6月2日（周三）12:00—13:00
地 点：东方语学院文献中心（7101）
主讲人：张若一博士
主 题：文学博士论文的写作经验
主办方：教师发展中心、东方语学院
简 介：张若一，男，1988年生，南开大学、耶路撒冷希伯来大学联合培养文学博士，美国宾夕法尼亚大学访问学者，上海外国语大学文学研究院助理研究员，主要研究方向为希伯来文学、比较文学。承担项目“文士文化与希伯来先知文学”获第66批中国博士后科学基金面上资助（编号2019M661586）。国家社科基金重大项目“希伯来文学经典与古代地中海文化圈内文学、文化交流研究”（项目编号15ZDB088）之子课题负责人。张博士将介绍本人博士论文《古代近东历史文献视阈下的以色列历史文学传统研究》的从选题、收集材料到修改的写作过程及注意点，并回答相关问题。

In this study, we examine the reputational costs of corporate tax avoidance, based on the recognition of well-known trademarks in China. The recognition of well-known trademarks marks a sudden and significant increase in the popularity of a firm’s products and, hence, provides an ideal setting to test whether firms bear reputational costs in tax avoidance. Using a variety of identification strategies aiming to address endogeneity concerns, we find that firms with well-known trademarks exhibit a lower level of tax avoidance, and this effect is concentrated in firms that are more sensitive to reputational damages, firms located in regions that show high levels of social trust, firms subject to stricter tax enforcement, and firms that are not owned by the government. These findings support the notion that firms recognize ex ante reputational costs in tax avoidance. In terms of ex post reputational costs, we find that tax avoidance is associated with negative media tones referring to the firms that possess well-known trademarks. We also find similar evidence on governmental sales. Overall, our study contributes to the tax literature by documenting evidence on the reputational costs of tax avoidance from a tax jurisdiction featuring distinct institutional and cultural factors.

内容提要：每部小说都属于在一个特定的空间，这个空间往往是其情节所跨越的地方的总和。于是空间被定义为小说动作发生的环境。新小说赋予空间一种行动功能。人物受困于其中，持续时间可能只有几秒，也可能跨越一生。因此，作为小说构成要素之一的“描述”，在小说的动态中获得了主导地位。
因此，我们可以质疑空间的非人性特征，人们努力在其中寻找自己的位置。这是否表明人与他所居住的世界之间出现了分离？其原因是什么？有什么后果？人重新渗透进环境的条件是什么？
我们将从以下角度分析：
1.所选空间的性质和功能
2.描述的详尽性
3.“描述”中明显的动机缺乏
4.对无足轻重的地点的偏爱
5.描述性方法代替叙述性方式
Résumé : L'objectif de cette conférence est de présenter : l’espace dans le Nouveau Roman
Tout roman s’inscrit dans un espace, qui le plus souvent est la somme des lieux traversés par son intrigue. L’espace est alors défini comme le décor à l’intérieur duquel se déroule l’action romanesque. En inversant les données, le Nouveau Roman confère à l’espace une fonction actantielle. Les personnages sont prisonniers des lieux dans lesquels ils sont contraints de séjourner, pour des durées qui peuvent ne correspondre qu’à quelques instants ou impliquer une vie entière. Aussi, la description, l’une des composantes constitutives de l’alchimie romanesque, acquiert une importance prédominante au sein de la dynamique fictionnelle. Il conviendra donc de s’interroger sur le caractère déshumanisé de ces espaces où les hommes peinent à trouver leur place. Est-ce l’indice d’un divorce entre l’homme et le monde qu’il habite ? Quels sont les causes de ce divorce ? Quelles en sont les conséquences ? Quelles pourraient être les conditions d’une osmose retrouvée entre l’homme et son environnement ? I. Natures et fonctions des espaces représentés
II. Exhaustivité des descriptions
III. Apparente gratuité des descriptions
IV. Prédilection pour l’insignifiance des lieux représentés
V. Substitution du descriptif au narratif

一、课程简介 | Course Description
几世纪以来，人文科学一直是针对特定文化主题进行“精读”（close reading）的，即通过精读揭示一层层含义，从而获得深层次的理解。然而，这种精读方法正越来越多地被基于计算机建模和语料库语言学的“远读”（distant reading）方法所取代，使得研究人员能专注于比单一案例/文本/作者更小或者更大的要素，如词汇、话题、体裁、主题等。
For centuries, the humanities has operated through the close reading of cultural objects: reading to uncover layers of meaning that lead to deep comprehension. Such ‘close’ approaches are increasingly replaced by ‘distant’ methods that rely on programmatic modeling and corpus linguistics. This allows researchers to focus on units that are much smaller or much larger than the singular case study, text, or author – words, topics, genres, themes, and so on.

在社交媒体背景下，“精读”和“远读”尤其相关。社交媒体的特点之一是虚假信息（disinformation ）的传播——例如后真相（post-truth）、过滤气泡（filter bubbles）和点击诱饵（clickbait）。网络上，内容的传播方式似乎更多的是病毒式的、争议式的，而非通过对话传播真实信息。我们该如何理解网络平台上的大量社交数据，才能揭示其背后的意识形态、偏见和争议呢？
Close and distant reading are especially relevant in the context of social media, which are marked by the spread of disinformation – captured in terms such as post-truth, filter bubbles, and clickbait. The visibility of online content often seems to be informed more by virality and controversy than by truthfulness and dialogue. How can we understand the large quantities of social data on online platforms in order to reveal ideologies, biases, and controversies?

在本课程中，我们将利用社交媒体数据来揭示意义的构建模式。运用多种文本分析和数据分析方法（如tf-idf、主题建模和词嵌入等），学生将学会以批判性和探索性的方式，对语料库语言学研究方法进行应用和思考。我们会重点探讨网络社区内事实和意见的构建方式，以及在自然语言中所呈现的模式和偏见。
In this course, we will engage with social media data in order to uncover such patterns of meaning-making. Using a variety of strategies of textual and data analysis (e.g. tf-idf, topic modelling and word embeddings), students will learn to apply and critically reflect on corpus linguistics with a critical and explorative mindset. We will focus on the discursive ways in which facts and opinions are negotiated within communities and the patterns and biases that appear in natural language.

二、课程目标 | Learning Outcomes
--能够认识及了解几种流行的文本分析定量方法，及其各自在认识论上的优劣。
Attain knowledge and understanding of the epistemological potentials and pitfalls of several popular quantitative approaches to text analysis.

--能够运用文本分析和语言分析方法，对当代社交媒体数据集进行分析。
Apply textual and language analysis methods to contemporary datasets taken from social media.

--能够认识定量方法框架中的规范和预设。
Demonstrate an awareness of the norms and presuppositions in quantitative methodological frameworks.

--能够以批判性和探索性的思维方式应用数字人文的量化研究方法。
Applying quantitative methods from the Digital Humanities with a critical and explorative mindset.

三、课程主题 | Course Outline
课程1：数字人文入门 Introduction
介绍Jupyter Notebook和代码仓库；学习Python编程基础。
Introduction to Jupyter Notebooks, class repositories; working through some programming fundamentals in Python.

课程2：Pandas & NLTK
学习Pandas库的DataFrames数据结构对社交数据进行基本数据处理；学习用NLTK库的Text class类对文本进行初步处理。
Exploring basic operations on Pandas DataFrames when dealing with social data. Looking at NLTK’s Text class that allows for initial exploration of texts.

课程3：主题建模 Topic Modeling
超越“作者”视角——学习主题建模方法，探索数据中呈现出的模式；运用主题建模获得的结果进行“精读”。
Exploring topic modelling as one way to move beyond the author and explore discursive patterns in our data. Using topic modeling findings to engage in close reading.

课程4：词嵌入 Word Embeddings
使用Python中的Word2Vec功能介绍词嵌入知识；对词嵌入模型中隐含的偏见进行批判性讨论。
Introducing Word Embeddings through Word2Vec in Python. Critical discussion about the concerns of bias implicit in Word Embeddings models.

课程5：回归分析 Regression Analysis
使用Python进行回归分析。
Regression analysis in Python.

课程6：Naïve Bayes和情感分析 Naïve Bayes & Sentiment Analysis
使用Python的NLTK库进行Naïve Bayes分类和情感分析。
Naïve Bayes classification and sentiment analysis using NLTK in Python.