Sights and Sounds of Joyful Resistance: Hip Hop, TikTok, and Bi+ Women's Identities
Bibliographic record
Abstract
This thesis examines the representation of women’s bisexuality in contemporary North American popular music and digital spaces, addressing how bi+ hip hop artists challenge reductive portrayals that contribute to bisexual erasure and stereotyping. It pursues three interrelated inquiries: how diverse women-identifying performers craft transgressive representations of bisexuality; how online affective archives on platforms like TikTok empower bisexual women to negotiate “third spaces” of resistance; and how redefining bisexuality as a transgression of normative boundaries can expand discourses on authenticity in queer and hip-hop studies. These inquiries are vital for understanding the ways in which people with marginalized identities can reconfigure cultural narratives and contest dominant media frameworks. Employing a mixed-methods design, the study is built around case studies of three artists, Ashnikko, Princess Nokia, and Snow tha Product. Synthesizing close readings of music videos, discourse analysis of digital fan interactions, and multimodal assessments of self-representation (analyzing visual, sonic, and textual elements across various media), each case study examines how artists and fans create affective archives on digital platforms (Cvetovich, 2003). In these alternative spaces, bi+ identities are historicized, validated, and reimagined as acts of joyful resistance – a process marked by playful self-celebration and a deliberate repudiation of heteronormative constraints on queer/bi+ people. This study finds that these cultural productions (including music, music videos, and social media content) not only subvert reductive narratives but also actively construct resilient counter-narratives, reframing contemporary discourses on sexuality, gender, and authenticity via a “bi+ worldview” (Nelson, 2023). In doing so, this dissertation contributes to feminist media studies, queer theory, and cultural studies by positioning bi+ representation as a dynamic form of feminist work, offering critical insights for ongoing discussions in both academic contexts and digital activist spaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".