Playing with Difference: Oyibo Lip-Sync Performances of Nigerian Popular Culture on TikTok
Bibliographic record
Abstract
Following the surge in global consumption of Nigerian music and film, non-African content creators are now part of the emerging group of people participating in the production and circulation of African, specifically Nigerian, popular culture on social media. This article provides a scholarly interrogation of these emerging “transracial” online enactments, with a particular focus on these performers’ use of lip-syncing to audio texts originally created by Nigerian artists. We examine the performances of three Oyibos (light-skinned foreigners) who create short videos on TikTok, arguing that through lip-syncing, each of them becomes part of networked publics built around Nigerian content and facilitated by the social media application TikTok. The article takes an audience-centred approach. Through focus group interviews with potential audiences in Nigeria, we seek to understand the meanings that Nigerian viewers (and potential members of these networked publics) derive from such clips and the transgressive play with difference that they entail.
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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 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".