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The significance of feminist textual analysis in literary and cultural studies, with a particular emphasis on employing data and code-based instruments to facilitate this approach, is examined in this paper. We rely on foundational feminist theories and methodologies of text analysis to advocate for feminist textual analysis as an essential tool for comprehending how gender and power dynamics impact the creation and interpretation of literature and other cultural artifacts.

We explore the ways in which computational methods and digital humanities tools can support feminist text analysis, including through the use of text mining, machine learning (for example: machine learning algorithms can be trained to identify and classify gendered language and stereotypes in texts, which can then be used to quantify and analyze patterns of gender bias and discrimination. This can enable feminist text analysts to more efficiently and effectively identify and critique problematic representations of gender in literature and other cultural artifacts) and other data-driven approaches.

The practice of feminist text analysis is multifaceted and can be approached from numerous theoretical perspectives. Its primary goal is to reveal how language perpetuates gender inequality and to find ways in which language can advocate for gender equality and social justice. However, several challenges exist in conducting feminist text analysis, including data roles, findings interpretation, and ethical issues related to language data handling.

Data plays a complicated role in feminist text analysis. Language is intricate and multi-layered, making it challenging to capture gendered language nuances solely through quantitative data analysis. Many feminist scholars propose that qualitative methods, such as discourse analysis or ethnography, are more appropriate for capturing the intricacies of gendered language use and provide a more detailed understanding of how language perpetuates and fortifies gender identities (Bucholtz & Hall, 2005).

Corpus linguistics is one method through which data can be applied in feminist text analysis. It’s a quantitative method that compiles large datasets of language use and analyzes language use patterns across various contexts (Baker, Gabrielatos, & McEnery, 2013). Corpus linguistics can identify gendered language patterns, such as gendered pronouns usage, frequency of gendered adjectives, and the distribution of gendered occupations or roles in different text types. A corpus linguistic analysis of job advertisements might show gendered occupations that uphold traditional gender roles, such as associating nursing with women and engineering with men (Prewitt-Freilino, Caswell, & Laakso, 2012). This type of analysis can help uncover instances of gender bias and inform efforts to encourage gender equality in the workplace.

With a significant amount of research funding being directed towards understanding the educational aspirations of individuals of different genders and nationalities, feminist text analysis is gaining popularity in various fields, including academia. As a researcher, I’ve been involved in data analysis initiatives funded by the Perkins grant, which aims to enhance secondary and postsecondary vocational and technical education programs in school districts and public two-year colleges. According to Perkins, one of the grant’s main objectives is to address performance discrepancies for special populations based on the Perkins core performance indicators.

Our data analysis efforts within the feminist text analysis context consistently focused on gender, ethnicity, and majors as vital variables. We study these factors to understand how they affect students’ retention and graduation rates. By employing feminist theories and perspectives in our analysis, we strive to reveal the intricate interplay between gender, power dynamics, and educational outcomes. Our research’s goal is to illuminate how gender inequalities manifest within academia and suggest interventions and policy changes to address these issues.

I’d like to share significant findings consistent with the Perkins grant’s objective of improving student success in foundational CTE courses, workplace readiness, and professional skills to boost student retention and completion rates. Our attention was primarily on retaining women in engineering technology programs and supporting students on academic probation. To achieve these goals, we launched a peer advisement and mentoring program. As a result, 80% of the women who received advisement (n=160) continued to be enrolled and progress in their respective study programs.

One of the crucial initiatives we launched to support the retention of women in engineering technology programs and students on academic probation was the planning of peer mentoring activities by the Women in Engineering Technology Faculty. These activities were specially created to engage women and students in the engineering technology departments, either virtually or in person. We enhanced student-faculty interaction across departments by leveraging OpenLab, an online platform. We also hired a coordinator and a team of peer mentors who played a pivotal role in this endeavor.

The peer mentors held weekly meetings with students, offering valuable mentorship, advice, tutoring, and tips on studying and time management. They also ran workshops to help students navigate college resources and make effective use of OpenLab. In the fall semester, twenty-three (23) peer mentors, along with the coordinator, mentored one hundred and ninety-four (194) students. In the spring semester, twenty-seven (27) peer mentors and one coordinator mentored ninety-six (96) students. Of these, 85 were associate degree students from various engineering technology departments, such as Architectural Technology, Computer Engineering Technology, Computer Systems Technology, Construction Management/Civil Engineering Technology, Electrical Engineering Technology, and Mechanical Engineering Technology. A remarkable 71.5% of these students either continued or received an associate degree in their respective programs.

To facilitate effective communication and support, the peer mentors used various online platforms such as Skype, Google Meet, and Zoom, with schedules and resources made available on the OpenLab platform. Peer mentors also engaged with students from Manhattan Bridge High School during a virtual Girl Day event in the fall and collaborated with elementary schools from PS 29 at a Super Science Day event during the spring semester. City Tech students also had the opportunity to attend the Women’s Builders Council Conference in June.

During the spring semester, the peer mentors switched to a hybrid model, with a portion of their hours being held in person. This shift led to a decline in student participation as some departments preferred virtual sessions to in-person meetings. Despite targeted email outreach to students on academic probation, the response was limited, with only eleven (11) students responding out of four hundred fifty-six (456) contacted.

These activities and outcomes underscore the importance of feminist text analysis in addressing gender disparities within engineering technology programs. By implementing peer mentoring initiatives, providing resources, and fostering engagement, we aimed to create an inclusive and supportive environment for women and students on academic probation, thereby enhancing their success and retention within their chosen fields.

However, it’s crucial to consider the challenges and limitations of feminist text analysis, including potential bias and the need for critical awareness of its limitations. While there are successful examples of feminist text analyses, like studies on gender representation in children’s books, the use of the word “hysterical” on Twitter, and the gendering of job titles in academia, the complexities involved must be acknowledged.These instances illustrate the potential of feminist text analysis in unveiling patterns of gender bias and inequality, thus contributing to gender equality and social justice promotion. Yet, one of the challenges lies in the selection and interpretation of data. Language usage is highly contextual, and words and phrases’ meanings can fluctuate depending on the social, cultural, and historical contexts they are used in. Hence, feminist text analysts must be conscious of potential biases introduced through data selection and interpretation. For instance, if a researcher only analyzes texts produced by a specific demographic, such as white, middle-class women, this might introduce biases that exclude other groups’ experiences and perspectives. Furthermore, data interpretation is also subject to the biases of the researcher. Different researchers may interpret language usage patterns differently based on their experiences and perspectives, whether they focus on gender identity or power dynamics issues. To overcome these challenges, feminist text analysts need to be cognizant of their own biases and strive to ensure their analysis is rooted in a diverse array of texts and viewpoints. Transparency in methodologies and assumptions is key, as well as being open to critical feedback and engagement from other scholars and communities.

Feminist text analysis is a powerful tool for exploring the creation and representation of gender in language. However, conducting such analysis requires careful consideration of data selection, interpretation, and potential biases. By studying a diverse array of texts and perspectives and staying open to feedback, feminist text analysts can offer meaningful insights into the dynamics of gender in language. The examples provided in this paper show how feminist text analysis can unearth gender bias, challenge dominant discourses, and contribute to the pursuit of gender equality and social justice.

Finally, feminist text analysis is of significant importance in literary and cultural studies as it promotes more inclusive and equitable representations of gender. To navigate the challenges it presents, continuous reflection, critical awareness, and commitment to embracing diverse perspectives are required. Going forward, in the context of our work, we plan to refine our outreach and engagement strategies, based on the learnings from our past initiatives. We believe that further investment in faculty development, peer mentorship, and the use of technology can yield better results in retaining women in engineering technology programs and supporting students on academic probation. As part of this endeavor, we will also conduct a feminist text analysis of student feedback and course materials to identify potential areas of bias and work towards creating a more inclusive and equitable learning environment. To expand our research scope and incorporate more intersectional perspectives in our feminist text analysis is crucial. This includes considering factors such as race, class, and sexual orientation, along with gender. Through this approach, we aim to provide a more nuanced understanding of the complex dynamics that shape educational outcomes and experiences. To summarize, while feminist text analysis presents its own set of obstacles, it is a vital instrument in the pursuit of gender equality and social justice. We hope to contribute to a more inclusive, equitable, and varied academic environment by exploiting the possibilities of this method in our work.

 

References

Bucholtz, Mary, and Kira Hall. Identity and Interaction: A Sociocultural Linguistic Approach, Discourse studies 7.4-5 (2005): 585-614. Oct. 2005, https://bucholtz.linguistics.ucsb.edu/sites/secure.lsit.ucsb.edu.ling.d7_b/files/sitefiles/research/publications/BucholtzHall2005-DiscourseStudies.pdf

McEnery, Tony, et al. “Discourse Analysis and Media Attitudes: The Representation of Islam in the British Press.” Academia.Edu, 23 Apr. 2021, www.academia.edu/47614385/Discourse_Analysis_and_Media_Attitudes_The_representation_of_Islam_in_the_British_Press.

Prewitt-Freilino, Jennifer L., et al. “The Gendering of Language: A Comparison of Gender Equality in Countries with Gendered, Natural Gender, and Genderless Languages.” Researchgate, Feb. 2011, www.researchgate.net/publication/257663669_The_Gendering_of_Language_A_Comparison_of_Gender_Equality_in_Countries_with_Gendered_Natural_Gender_and_Genderless_Languages.

P West, C., & Zimmerman, D. H. (1987). Doing gender. Gender & Society, 1(2), 125-151. www.gla.ac.uk/0t4/crcees/files/summerschool/readings/WestZimmerman_1987_DoingGender.pdf