Digital Research Methods for Translation Studies (Research Methods in Translation and Interpreting Studies)

✍ Scribed by Julie McDonough Dolmaya

• Publisher: Routledge
• Year: 2023
• Tongue: English
• Leaves: 276
• Edition: 1
• Category: Library

✦ Synopsis

Digital Research Methods for Translation Studies introduces digital humanities methods and tools to translation studies.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Foreword
Acknowledgements
Introduction
Aims and scope
Intended audience
Structure of the book
References
1 Methods, Data, and Tools
1.1 Methods and Methodology
1.2 Digital Tools
1.3 Data
1.3.1 Qualitative and Quantitative Data
1.3.2 Big Data
1.3.3 Datasets
1.4 Chapter Take-aways
Notes
References
2 Managing Research Projects and Organizing Research Data
2.1 Project Management
2.2 Organizing Research Data
2.2.1 Organizing References
2.2.2 Digitizing, Storing, and Organizing Research Data
2.2.3 Digitizing, Storing, and Organizing Multimodal Data
2.3 Chapter Take-aways
2.4 Applications
Notes
References
3 Open Data and Freedom of Information
3.1 Open Data Sources
3.1.1 Governments
3.1.2 The Private Sector
3.1.3 Multilateral, Non-Governmental, and Non-Profit organizations
3.1.4 Scholars and Research Institutions
3.2 Open Data Relevant to Translation Studies Research
3.2.1 Language Industry Datasets
3.2.2 Open Term Banks and Glossaries
3.2.3 Open Catalogues/Databases of Translations
3.2.4 Translation Memories
3.2.5 Corpora
3.2.6 Open Non-Text and Multimodal Data
3.2.7 Open Social Media Datasets
3.3 Ethical and Methodological Concerns of Open Data
3.4 Access to Information
3.5 Chapter Take-aways
3.6 Applications
Notes
References
4 Collecting Data from and through the Internet
4.1 Ethics and Internet-Based Data Collection
4.1.1 Informed Consent
4.1.2 Privacy
4.2 Methodological Concerns of Internet-Based Data Collection
4.3 Unautomated and Semi-Automated Data Collection
4.3.1 Crowdsourced Data Collection
4.3.2 Participatory Research
4.4 Automated and Semi-Automated Data Collection
4.4.1 Web Scraping
4.4.2 Legal and Ethical Issues Associated with Web Scraping
4.4.3 Scraping Websites with Python and R
4.4.4 Scraping Websites with Excel and Google Sheets
4.4.5 Scraping Websites with Other Tools
4.4.6 Examples of Web Scraping in Translation Studies Research
4.4.7 Application Programming Interfaces (APIs)
4.5 Chapter Take-aways
4.6 Applications
Notes
References
5 Data Preparation
5.1 Preparing Structured Datasets
5.1.1 Dataset Organization
5.1.2 Data Integration
5.1.3 Data Transformation and Extension
5.1.4 Data Cleaning
5.1.5 Relevant Tools
5.1.6 Ethical and Methodological Considerations
5.2 Preparing Translation Memories
5.3 Preparing Textual Data
5.4 Preparing Geographic Data
5.4.1 Geoparsing Textual Data
5.4.2 Geocoding
5.5 Preparing Audiovisual Data
5.5.1 Automated Speech-to-Text and Transcription Tools
5.5.2 Ethical and Methodological Considerations of Transcription
5.6 Preparing Visual Data
5.7 Chapter Take-aways
5.8 Applications
Notes
References
6 Analyzing Textual Data
6.1 Annotation Methods and Tools
6.2 Corpus Analysis Methods and Tools
6.3 Computer-Assisted Qualitative Coding
6.4 Other Quantitative Analysis Methods and Tools
6.4.1 Stylometry
6.4.2 Sentiment Analysis
6.4.3 Voyant Tools
6.4.4 Python and R
6.5 Chapter Take-aways
6.6 Applications
Notes
References
7 Analyzing Multimodal and Non-text Data
7.1 Annotation Tools
7.1.1 Annotating Websites and Web-Based Artifacts
7.1.2 Annotating Audio and Audiovisual Material
7.2 Computer-Assisted Qualitative Coding Tools
7.3 Automated Image Analysis
7.4 Chapter Take-aways
7.5 Applications
Notes
References
8 Analyzing Structured Datasets
8.1 Numeric Attributes
8.2 Nominal and Ordinal Attributes
8.3 Structured Dataset Analysis Approaches
8.4 Analyzing Structured Datasets with Spreadsheet Tools
8.4.1 Analyzing Numeric Attributes with Spreadsheet Tools
8.4.2 Analyzing Nominal and Ordinal Attributes with Spreadsheet Tools
8.5 Data Analytics Tools and Statistical Software
8.6 Chapter Take-aways
8.7 Applications
Notes
References
9 Quantitative Visualizations
9.1 Designing Quantitative Visualizations
9.2 Visualizing Trends and Changes Over Time
9.3 Visualizing Distribution Characteristics for Numeric Attributes
9.4 Visualizing Nominal and Ordinal Attributes
9.5 Creating Visualizations with Spreadsheet Tools
9.6 Creating Visualizations with Data Analytics Tools and Statistical Software
9.7 Creating Visualizations with Browser-Based Data Visualization Tools
9.8 Chapter Take-aways
9.9 Applications
Note
References
10 Qualitative Data Visualization
10.1 Design and Software Considerations for Qualitative Visualizations
10.2 Visualizing Decisions and Processes
10.3 Organizing and Categorizing Information (Relationships)
10.4 Visualizing Concepts and Theories
10.5 Visualizing Comparisons
10.6 Text-Based Visualizations
10.7 Time-Ordered Displays
10.8 Ethical and Methodological Challenges
10.9 Chapter Take-aways
10.10 Applications
Notes
References
11 Geographic Visualizations
11.1 Designing Geographic Visualizations
11.2 Tools for Generating Geographic Visualizations
11.2.1 GIS Software
11.2.2 Other Geographic Visualization Tools
11.3 Examples from Translation Studies and Beyond
11.4 Ethical and Methodological Challenges
11.5 Chapter Take-aways
11.6 Applications
Notes
References
12 Network Visualizations
12.1 Designing and Analyzing Network Visualizations
12.1.1 Designing and Analyzing Social Network Visualizations
12.1.2 Designing Bibliometric Networks
12.2 Tools for Generating Network Visualizations
12.2.1 Social Network Visualizations
12.2.2 Bibliometric Network Visualizations
12.3 Chapter Take-aways
12.4 Applications
Notes
References
13 Web Presentations and Open Data Repositories
13.1 Web Presentations
13.2 Open Data Repositories
13.3 Metadata
13.4 Chapter Take-aways
13.5 Applications
Notes
References
Conclusion
Where to Start
Some Final Thoughts
References
Index