Fashion studies is an interdisciplinary academic field. We believe it strongly bridges to social science areas including sociology, anthropology, and psychology. People who came from fine arts and design studies as well as those who have social science background of studies will show integrating interests to our project.
1. Socioeconomic scholars
They will analyze data from instagram based on geographic information, neighborhood, and economic status. Instagram users tag their location and brand of clothes once they post their ootd (outfit of the day). Based on the information, socioeconomic scholars can predict the users’ economic climate. Depending on neighborhoods and districts, people dress differently. Thus, dress code symbolizes the occupations along with the social classes. Certain clothes are dressed in special occasions to discuss social or political issues and to give messages.
Fashion is a form of expressing oneself. Psychologists can predict what certain groups of people want to express through their clothing. They can also read the psychological fabrics of the people through patterns, colors, and styles. What’s more, fashion is deeply related to the psychology of the consumers, and psychologists can analyze and predict its trend. Personal style without chasing the trends defines one’s identity through what one is wearing and reflects strong points what looks good on one’s body shape.
3. Costume Designers
They work on the design of items of clothing and pay attention to specific reference materials especially textiles and colors. They will get inspiration to design their clothes for the performers especially from film and broadcasting. In particular, costume designers are responsible for overall look of the clothes and costumes in theatre, film, or television producers. They need to have excellent design skills as well as organization skills to lead the team. They should also reflect the socioeconomic and psychological aspects of the people’s life in different backgrounds.
In this project, we aim to provide straight facts about the who, what, when, where. The tags will be listed by either the initial user or added by crowdsourcing.
User Story #1: A forensic computational linguist doing research on how interviewing style impacts witness responses. The value of the site to the user is being able to compare friendly vs. unfriendly witnesses (difficult to determine in general court transcripts) and the sheer number of available court transcripts available (also difficult to collect re general court transcripts). The person clicks the API link and follows the prompts to extract a cluster of readings from unfriendly witness testimony, and does a second export for a cluster from friendly witness testimony. The API exports the two corpora into an intermediary location (such as Zotero), which can be used with Python (NLTK) to compare, for example, the number of times interviewers repeated question for friendly vs. unfriendly.
User Story #2: High school civics & US history teacher, Chris. He is wants to assign the students to search the archive to find primary source documents from the McCarthy era. Students will have a list of topics and names to choose from as their research areas. Chris tests the site to see if it will be useful to his students. Chris uses the simple search box to search for both topics such as ‘treason’ and ‘democracy’. Chris uses the advanced search options to combine topics with names. Chris is looking for clean results pages, the option to save and export searches, and help with citation.
User Story #3: American Political Scientist, Jennie, doing research on US government responses during periods of perceived national security threats. Specifically, she is interested in the Foreign Intelligence Surveillance Courts (FISC), which are closed courts. Jennie wants to read about now-released documents that record the conduct of closed courts. Jennie wants to do mixture of qualitative and quantitative analysis. Qualitative: Jennie uses the advanced search to specify she wants only to look at hearings that have been identified in the category as ‘closed’ hearings, then does the same to specify only ‘open’ hearings. Quantitative: Jennie uses API and follows the prompts to extract data with the category ‘closed’ and date filter to do a statistical analysis of number of closed trials by year and how/if correlated to outside events.