Deconstructing Desire: Criticism of Western Romantic Narratives in Mitski's "Your Best American Girl" Music Video
Bibliographic record
Abstract
Mitski Miyawaki, a Japanese American indie-rock artist professionally known as Mitski, wrote her 2016 song, “Your Best American Girl,” from the perspective of a woman who is unable to have a relationship with her love interest due to their different racial and cultural backgrounds. The accompanying music video engages with the song’s social message while adding nuance and complexity to it. Many of the lyrics portray Mitski’s feelings of isolation as an Asian American woman, especially through their employment of Japanese cultural symbols, while the music video uses parody, camera angles, and Americana iconography to further illustrate Mitski’s experiences of isolation. This essay analyzes the subtle ways in which “Your Best American Girl” subverts Asian stereotypes and destabilizes white patriarchal structures that are perpetrated by popular media, particularly through white centrality in the indie-rock genre. Comparison of “Your Best American Girl” to Lana Del Rey’s “Born to Die” music video reveals how “Your Best American Girl” uses parody techniques to criticize this white centrality. Further, its references to PJ Harvey allows Mitski to occupy a similar position of musical authenticity and command respect. Through lyrical, musical, and visual storytelling, “Your Best American Girl” chronicles Mitski’s journey towards self-acceptance, while critiquing the pervasive whiteness in romantic narratives.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.040 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".