Bibliographic record
Abstract
This study is an exploration of identity politics through an examination of the ways in which musical artists use the medium of music videos to create marketable, hybrid identities. With the rise of social media and the online consumption of information, music videos play a central role in global, cultural flows. I argue that hybrid identities are constructed by musical artists to gain popularity through the form of ethno-marketing. I include literature surrounding diaspora and hybridity to understand how hybrid identities become a production of heritage and human capital. By utilizing music videos specifically to construct their hybrid identities, musical artists are simultaneously enforcing and being subjected to economic, cultural, and political forms of exploitation. My methodology draws upon a multimodal discourse analysis (LeVine & Scollon, 2004) which assesses how meaning is made through the use of multiple modes of communication. I apply multimodality to the construction of music videos in which musical artists selectively chose particular sounds, images, and lyrics to claim specific identities. As articulated through the case study of Drake, I examine how the multimodal affordances of music videos allow artists to transcend borders within the digital age and reach a large audience. This study examines Drake’s bricolage of complex and intersectional identities and his unique privilege to choose to identify with different marginal communities. I assess how Drake capitalizes on shared experiences and struggles of different cultural, national, and class backgrounds though three of his music videos: “HYFR (Hell Yeah Fuckin’ Right)” (2011), “Started From The Bottom” (2011), and “Worst Behavior” (2013). Drake alludes to different cultures, locations, and social identities through these music videos to construct his place as a rapper in the music industry and articulates a hybrid identity as an “Authentic” Black/ Jewish, American/Canadian, working class member of society, and high-class rapper.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".