Welcome back to my blog!
What is new?
During the time of my last blog, I worked on finalizing my methodology and now moving on to my official data collection. I did finalize my film list: The Breakfast Club (1985), Can’t Buy Me Love (1987), and Heathers (1989). For the 90s category: Clueless (1995), She’s All That (1999), and Ten Things I Hate About You (1999). Next, the 00s category included: Freaky Friday (2003), Juno (2007), and Mean Girls (2004). Lastly, the 2010s category: Call Me By Your Name (2018), Lady Bird (2017), and Easy A (2010).
To commence my data collection, I had to create a criteria that would define what would be considered a count towards the frequency of Male Gaze present.
The criteria of Male Gaze created can be split into three categories:
Characterization: the way that the characters are talking and portrayed through the writing and conversations in the film. In this section, the following were looked for: Male characters talk treat female counterparts as sexual objects, Male characters attempt to appear as “macho hero” type, and embrace toxic masculinity and rejection of LGBTQ+ characters.
Point of view: Who’s values, desires, thoughts, identity, and feelings are showcased in a scene or film in general? It evaluates the female, male, and queer perspectives at hand, and their relationship to the male gaze. With that in mind, the section accounted for the following: Feelings, thoughts, and sexual desires of female characters are less important compared to male counterparts, Shots made through the POV of the male character, and do not acknowledge homosexual or queer gazes while Female LGBTQ+ interactions are for male pleasure/fetishization.
Technical: entails stylistic choices, like movement from the camerawork or clothing used on characters, by the directors. The technical category noticed: Unnecessary amounts of nudity seen in female characters, Slow panning of a woman's body, and Female characters wearing more revealing/occasion-inappropriate clothing compared to their Male Gaze.
At the moment I am still watching my movies, and note-taking examples of Male and/or Female Gaze examples.
I have watched half of the movies, not in a particular order, since I randomly selected all the movies to determine the order in which they were analyzed
So far I have completed the data collection of: Lady Bird (2010s), Mean Girls (2000s), She’s All That (1990’s), Heathers (1980s), Freaky Friday (2000s), 10 Things I Hate About You (1990s), and Clueless (1990’s)
Scene from movie Call Me By Your Name.
I am continuing my moving watching process with Call Me By Your Name, starring Timothee Chalamet.
This movie is a 2010s decade movie, and slightly different in the way that it stars a male protagonist who also doesn’t follow the societal beauty standard describing the male gaze. It also presents the beautiful love story between two male protagonists which strongly misaligns with the male gaze that almost exclusively showcases heterosexual relationships.
As for what is going to be completed next, the other half ½—⅓ of the movies will need to be completed at a quicker pace as the date for my presentation is April 13th.
Next blog post my data will have been collected and I will commence the data analysis process
I have made the choice to create movie watching data collection sheets
In these sheets, there is just a brief list of tropes that are found in the films. However, this data will not necessarily be included in the paper/presentation it is more for additional information as well as some helpful hints on what scenes to place heavier emphasis on.
Next, there is a list of scenes labeled with their timestamp that presents what happened within that part.
Once all the movie data sheets have been completed, they will be inserted into a 2-way table in order to conduct a chi-squared test. On one axis there will be the different decades and then on the other will be two categorical variables: The Male gaze and the Female Gaze
After speaking to my AP statistics teacher, I found that transferring my data into two way table will be the easiest method for quantifying my data, given that most of my data is qualifying
No significant changes have been made to the current methodology instead additions have been made as covered above :)
Image links and references
https://www.imdb.com/title/tt4925292/ (image carousel, Lady Bird)
https://www.imdb.com/title/tt1282140/ (image carousel, Easy A)
https://www.imdb.com/title/tt5726616/ (image carousel, CMBYN)
https://m.imdb.com/title/tt0467406/fullcredits/cast (image carousel, Juno)
https://www.imdb.com/title/tt0377092/ (image carousel, Mean Girls)
https://www.imdb.com/title/tt0322330/ (image carousel, Freaky Friday)
https://www.imdb.com/title/tt0147800/ (image carousel, Ten Things About You)
https://www.imdb.com/title/tt0160862/ (image carousel, She's All That)
https://www.imdb.com/title/tt0112697/ (image carousel, Clueless)
https://www.rottentomatoes.com/m/heathers (image carousel, Heathers)
https://www.imdb.com/title/tt0088847/ (image carousel, The Breakfast Club)
https://www.imdb.com/title/tt0092718/ (image carousel, Can't Buy Me Love)