"Bad Gal" and the "Bad" Refugee: Refugee Narratives, Neoliberal Violence, and Musical Autobiography in Honey Cocaine's Cambodian Canadian Hip-Hop
Bibliographic record
Abstract
This project employs a close textual reading of Cambodian Canadian hip-hop artist Honey Cocaine's 2016 music video "Bad Gal." Drawing from the fields of Critical Refugee Studies, comparative racialization, and neoliberal critique, I delineate the processes of gendered racialization for the Cambodian diasporic subject, and begin to unpack its racialized relationship to Blackness. In observing "Bad Gal" for its audiovisual content, temporal narrative, themes of deviance and Blackness, as well as supplemented by historical and spatial contexts, and interviews with Honey Cocaine, I argue that the construction of the "bad gal" or "bad refugee" persona is racialized through the genre of hip-hop and Blackness, and acts as a way for the Cambodian diasporic subject to negotiate against neoliberal logics and binary discourses of the "good" versus "dysfunctional" refugee. Through engaging with a cultural studies lens, this project encourages a reading of Asian diasporic hip-hop that complicates static understandings around authenticity, appropriation, and race relations, and to read the texts for their contradictions in revealing the ways it negotiates systems of neoliberalism, rather than to assess work for their "critical" or "politically resistive" value.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".