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Non-sexist language in Russian: exploring YouTube with MAXQDA - MAXQDA
While public discussion concerning non-sexist language has existed in Europe for several decades, in Russia it remains at its early stages.
There are almost no documents and guidelines with recommendations regarding non-sexist language and public perception of proposals to create more non-sexist language is also widely unknown. Analysis of the public perception of non-sexist language seems promising, because it might shed light on the perspectives of this topic in Russia. With this goal in mind, I have conducted an analysis of YouTube comments as a part of my doctoral research.
As one of the most commonly-viewed websites on the internet, YouTube is often used in the course of sociological and sociolinguistic research, which was the reason for me to choose this platform dissertation language sexist my analysis. The main research method was qualitative content analysis.
Qualitative content analysis is understood here as a research technique in which a fixed in this case — written communication is analyzed in a systematic, rule-guided and theory-driven manner, with the aim of drawing conclusions about certain aspects of the communication cf. Mayring,S, dissertation language sexist. The analysis was conducted with MAXQDA and included five notable steps, which I will describe below.
First off, I had to import the data from YouTube into the program. Fortunately, it was a very easy thing to do, because MAXQDA has a special button for YouTube import in the Import Tab. Along with importing the comments MAXQDA automatically autocodes them according to the number of responses, which was very convenient for me and helped dissertation language sexist the discussion while coding.
Figure 1: After inserting the URL of the video MAXQDA automatically collected all of the video information. Next, I wanted to get the first overview of the data and created a Word Cloud where the most frequent words were displayed. Figure 2: Word Cloud is a good option for creating the first overview of the data. Youtube Data Import Word Clouds. After looking at the words presented in the Word Cloud, dissertation language sexist, I started looking dissertation language sexist other ways of summarizing my data and having various samples of contexts.
They are also the main topic of both of the videos. As a result, contexts have emerged, which helped me to gain an overall idea of the data and start with the manual coding.
Figure 3: Memo in the code system automatically created for the autocode. Lexical Search Autocoding. In my research I used an inductive category definition, which means that the categories are derived directly from the material by generalizing the material cf.
Mayring, There was no previously theoretically conceived category system. The coding took several rounds. First, the system of categories was created after context sampling see above and then this system was adjusted after each round of coding, dissertation language sexist.
Since I was not working in a team and there was no possibility of cross-checking, multiple rounds of coding were essential for quality control, dissertation language sexist. Figure 4: The code system included autocodes and the codes created by me. In order to finetune my code system I used the Smart Coding Tool, because it allowed me to dissertation language sexist assign new codes to text segments, change the code system, move the codes in the hierarchy, merge codes, or create a new subcategory.
The Smart Coding Tool was also the place where I most often created memos in which I explained the meaning of each particular category and its difference from the other categories.
Some of the memos later served as dissertation language sexist basis for the text of my dissertation chapter. Figure 5: Smart Coding Tool helped me with the finetuning of my codesystem, dissertation language sexist. Figure 6: Memos were a place where I described the special characteristics of each category. Smart Coding Tool Code Memos. After all these steps I was finally ready to analyze the results and describe the research in my thesis.
For the sake of the transparency of my research this data will later be added as an appendix to my dissertation. Through the analysis, a total of sixteen categories emerged, nine for the arguments against non-sexist language and seven for the arguments supporting it.
Overall, in the comments there were over twice as many arguments against non-sexist language than those supporting it statements vs. However, dissertation language sexist, there may be several explanations for this, apart from the conclusion that non-sexist language is met with a negative attitude.
In general, dissertation language sexist, negative comments and criticism are very widespread on YouTube cf. Of the nine counter-arguments from the present work, five are consistent with the dissertation language sexist proposed by Parks and Robertonwhich means that there are some commonalities between the discussion in Russia and in other countries.
Chambers, B, dissertation language sexist. International Journal of E-Politics3 2dissertation language sexist, 1— Koeser, S. Promoting Gender-Fair Language: The Impact of Arguments on Language Use, Attitudes, and Cognitions. Journal of Language and Social Psychology33 5— Mayring, P. Qualitative Inhaltsanalyse: Grundlagen und Techniken Parks, J. Contemporary Arguments Against Nonsexist Language: Blaubergs Revisited. Sex Roles39 5— Login MAXQDA TeamCloud Dissertation language sexist Subscription Manager.
Other languages Deutsch Český Español Français 中文 简体 中文 繁体 Italiano 日本語 Polski Português Русский Svenska Türkçe. Why MAXQDA? MAXQDA Research Blog. Back to Overview. What is non-sexist language and does it even exist in Russia? Step 1: Importing data from YouTube and the first overview of the data First off, I had to import the data from YouTube into the program. Figure 1: After inserting the URL of the video MAXQDA automatically collected all of the video information Next, I dissertation language sexist to get the first overview of the data and created a Word Cloud where the most frequent words were displayed.
Figure 2: Word Cloud is a good option for creating the first overview of the data Youtube Data Import Word Clouds. MAXQDA Newsletter Our research and analysis tips, straight to your inbox, dissertation language sexist. By submitting the form I accept the Privacy Policy. Similar Articles Understanding Social Determinants of Health: Coding WHO policy papers for central themes Cómo realizar nubes de palabras con MAXQDA: paso a paso MAXQDA Photo Contest Winners and Honorable Mentions Reinforcing the Afghan Diaspora in the Digital Age.
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