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Neoliberal feminism has gained significant popularity in fourth-wave feminist media. In this paper, I analyze the 2017 limited television series "Big Little Lies" to uncover the intricacies of neoliberal feminist theory in practice, particularly how it speaks to gender, race, and class relations.
ContributorsLuther, Molly E (Author) / Moran, Stacey (Thesis director) / Henderson-Singer, Sharon (Committee member) / Arts, Media and Engineering Sch T (Contributor) / School of Social Transformation (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Although Spotify’s extensive library of songs are often seen broken up by “Top 100” and main lyrical genres, these categories are primarily based on popularity, artist and general mood alone. If a user wanted to create a playlist based on specific or situationally specific qualifiers from their own downloaded library, he/she would have to hand pick songs that fit the mold and create a new playlist. This is a time consuming process that may not produce the most efficient result due to human error. The objective of this project, therefore, was to develop an application to streamline this process, optimize efficiency, and fill this user need.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
ContributorsDiMuro, Louis (Author) / Balasooriya, Janaka (Thesis director) / Chen, Yinong (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Identity is something that I considered to be concrete and true, but I never really questioned my own identity until I was faced with solitude and self reflection. Throughout this essay, I will explain my journey through identity and my realization that there is no stable self and there never will be. To reach this conclusion I defined Identity and different theories that are associated with it, while also analyzing texts from theorists such as Nietzsche and Butler. I was able to reflect on my own identity and embark on an endless voyage of questioning while conducting this research. This inspired me to create an art installation that depicts my discovery while also displaying the painful mourning of a Self that I never really had.
ContributorsElam, Miranda Michelle (Author) / Moran, Stacey (Thesis director) / Cheyne, Rebekah (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05