Blog Post 2

Context 

Considering that fairy tales are one of the most widely consumed pieces of literature by children, it is also important to see how the movie versions of these beloved childhood tales can affect a child's self-perception. It has been seen in past research how the representation of people of color in real life has affected children in the past, most popularly known with the Doll study that was conducted in the 1940s by Kenneth and Mamie Klark that I mentioned in my previous blog post. When this research was reconducted in a more modern setting, the researchers were able to note that there was a better sense of self-perception due to the children having more positive models of people of color throughout their early development. Since fairy tales are critical in a child's early development stage, this is why I chose to focus on that for my project.

A paper from 2022 especially talks about how Disney Princesses are portrayed and how that affects children. They went through 8 Disney princess movies and explained how the minorities were portrayed in them, and how this can affect the younger generations. Papers and research like these is what led me to my research project, as I wanted to address a slight gap that appeared in these.                                                                             

Progress

There has been a lot of changes since the beginning of my research. After I started conducting my research, I started to realize that due to the fact that all the films I was watching followed what was relatively the same storyline, there was not much room for there to be "stereotypical" or "positive" representation in any of the films. Due to this, I decided to scrap my data collection altogether and start over. 

My new data collection included 2 main categories, main characters (characters that were essential to the plot line), and background characters (characters that did not aid to plot and were only seen briefly). The main characters were also separated into positive and negative groups. With this data collection model, it was much easier to categorize the characters that I noted, while also leaving out what could've been potential bias. 

I've also chosen to cut down on the amount of movies I was watching. Initially, I had chosen 19 movies to watch, but along with not being able to find some of the movies that I had initially chosen, it was also more difficult to do 19 movies as I had started over my model. My new model includes 15 movies, and with these I have found all of the movies except for one that I am still searching for.

This new data collection is going well for me, and I am much less conflicted when trying to categorize my characters into their respective categories. Although this means that I am a little behind in my data collection, I think that I am doing relatively well now. 

 <-- On the left (Fig. 2) is a photo of me conducting my data! My movie is on the right and my data collection is on the left on a google spreadsheet so that I can keep track of it all! Although I do some data collection at school, the majority of my data collection is done at home.

My updated list of films and updated methodology is shown below.