D ATA ORGANIZATION AND ANALYSIS

After identifying what would be considered compliments and insults in my study and how I would categorize them, I had to go through the data material and identify and organize all relevant evaluations, and then analyze them. These processes will be outlined in the following sections.

3.5.1 Organization

In order to identify and organize the evaluations in the data material into the abovementioned categories, I used NVivo. NVivo is a qualitative data analysis computer software, which is primarily a coding software that allows for researchers to organize and analyze qualitative data, such as open-ended interviews and journal articles.

Before the evaluations could be categorized, though, they had to be identified. The recognition and classification of speech acts is challenging, as there are no linguistic formulas dedicated to each category. Levinson (2017, 208) offers two distinct components required in recognizing and comprehending speech acts: bottom-up information and top-down information.

Bottom-up information refers to the form of the utterance, such as lexical choice, construction, and intonation, which can give clues as to what speech act type the utterance is. However, as disclosed in section 2.2.1, one utterance can have several different functions depending on other factors, such as the context and body language. There is, in other words, not always a direct correlation between form and function. Hence, top-down information also needs to be taken into consideration. Top-down information contains all available context and sequential information (Levinson 2017, 210). This includes information about where the conversation is taking place, who the participants are and their relation, what has been said before, and the addressee’s response.

Several evaluations only required the bottom-up information, as they were intuitively identified as either compliments or insults simply by looking at sentence structure and lexical choice, such as “you look good” (13RW-01) and “you’re a dumbass” (13RW-06). However, some evaluations required top-down information in order to be detected, such as “does he need his diaper changed?” (13RW-03). Out of context, this question does not seem like an insult, but when the context is taken into consideration, a group of guys making fun of Alex, who has sustained a brain injury, the question is immediately interpreted as an insult, as it insinuates that Alex is no longer able to take care of himself. This is further supported by the fact that the same group of guys uses the word crippled to describe Alex on other occasions. Because bottom-up information alone was not always enough to identify evaluations, I also used the top-down information available through Netflix on all potential evaluations to ensure that all relevant evaluations were identified.

Simultaneously, as I identified the relevant evaluations, I also organized them in NVivo according to the categories outlined in section 3.4. The audio-visual context available through Netflix was also useful during this process in order to determine to what category and subcategory each evaluation belonged. This was also particularly helpful when trying to identify whether compliments were sincere or not and whether insults were said jokingly or not.

After organizing the evaluative moves in NVivo, I reorganized them in separate excel spreadsheets, one for each main category or large subcategory, such as Emotional intelligence

and General traits. In each spreadsheet, I included the following supplementary information about the evaluation and the context:

- gender: whether the giver and target are male or female.

- sexual orientation: whether the giver and target are heterosexual or queer

- target involvement: whether the target of the evaluative move is present, not present, or dead - giver: who performs the evaluative move

- target: who the evaluative move is directed at

- addressee: who the compliment or insult is spoken to, regardless of whether they are the target - subcategory: what subcategory the evaluation belongs to

- further reflections: such as otherwise useful context information, function, or implication of the evaluation.

These additional brackets of information both allowed me to understand the context of most evaluations without consulting the transcripts or series themselves, and to organize the evaluations in a multitude of different ways in order to count them and detect potential patterns.

For example, the spreadsheets allowed me to organize the evaluations by compliments, speaker, and target, simultaneously, which helped me visualize how many compliments of a certain variety are given to female characters by other female characters in comparison to male characters. In order to achieve this organization, I made a coding system for the speaker, target, and addressee brackets which included the gender and sexual orientation of the characters before their names (e.g., FHE Veronica = female heterosexual Veronica, MQ Tony = male queer Tony). In the cases where the addressee is an adult, they were simply labeled with their names or profession, as the addressees were not of interest in my study.

To further simplify the analysis process, I color-coded the supplementary information that would be involved in the analysis: gender, sexual orientation, giver, target, target involvement, and subcategory. The subcategories were color-coded in line with their main categories, e.g., the main category Skills was assigned the color blue and every subcategory of Skills was a different shade of blue. The givers and targets were color-coded in terms of their gender and sexual orientation. The female characters were marked in yellow and the male characters in green. I did not need a non-binary gender category because all the teen characters in the episodes included are portrayed as cisgender. The queer characters were marked with the color corresponding to their gender, but in one shade darker so that they could also be analyzed separately if that proved to be of interest. It was also necessary to be able to see whether the evaluations related to Sexuality are directed toward queer characters or not. As for the target involvement, the cases where the target is present or not present were not color-coded.

However, in the cases where the target is dead, the text was colored red so that those evaluations could easily be distinguished from the others (see section 3.5.2).

After reorganizing the evaluations into excel spreadsheets, I contemplated whether it would have been more efficient to organize the evaluation into a spreadsheet immediately, instead of using NVivo first. However, NVivo allowed for a much quicker categorization with their code function. Additionally, NVivo made it easy to find the evaluation in the reference file in order to see it in context or to see in which episode and approximately when in the episode the quote is located. These features made both the initial categorization process more efficient, and the open-reference-file function served as a useful tool when analyzing the material in the excel spreadsheets because I could easily find the quotes in NVivo which would either provide me with the context needed or the information of where to find the necessary context in the series on Netflix.

3.5.2 Analysis

The analysis process was divided into three steps: one using a quantitative approach and two using a qualitative approach. The step using the quantitative approach entailed counting how many compliments and insults were given and received by each gender within each category and subcategory. The numbers were added to an Excel spreadsheet in order to calculate what gender gives and receives the most compliments and insults within the different categories. In order to account for the difference in how many male and female characters are included and the difference in how much they speak, I chose to focus on the average numbers and how many percent of the lines spoken are evaluations when making comparisons.

Additionally, the fact that a few of the evaluations were placed in more than one category also had to be taken into consideration (see section 3.4). Therefore, when discussing the distribution of the evaluations according to gender, i.e., how many evaluations the male and female characters give and receive each, the duplicated evaluations were subtracted from the total number of evaluations categorized. When discussing the number of evaluations according to category placement, on the other hand, all evaluations were counted, including the duplicated ones. Thus, the number of evaluations will be slightly higher when discussing the categories than when discussing the evaluations in general.

The qualitative approaches to the data material, firstly, entailed going through the compliments and insults and deciding which category they belong to and, secondly, examining the correlation between the evaluations and the gender stereotypes discussed in section 2.4.

One aspect of the quantification of the evaluations that had to be taken into consideration was the fact that some of the evaluations were made about dead characters, both in everyday speech and in court hearings, which could potentially skew the evaluations toward more compliments than if the characters were alive. This is the case for two peripheral characters, one in Riverdale and one in 13RW, and one main character in 13RW, Hannah, who committed suicide, which in turn is the reason for the court hearings. Because the evaluations of these characters could potentially skew the results toward more compliments for the female characters, I decided to calculate my results, when relevant, both with the evaluations made of the dead characters included and without them to check to what extent the results were affected.