Virtual Influencers and the New Wave of Digital Labour Exploitation
Bibliographic record
Abstract
Virtual influencers (VIs) are animated replacements for human social media influencers, with popular VIs like Lil Miquela garnering millions of followers. This thesis explores the unaddressed ways VIs enable the exploitation of the human labourers creating them. As human influencers have become more powerful and expensive to work with brands and marketers have sought to regain control over them. The behind-the-scenes workers creating VIs have limited ownership of the characters they create, and a system of NDAs, job insecurity, and exploitation of worker passion discourages workers from discussing labour conditions. These conditions complicate primary research on VI creators, pushing me towards influencer studies and digital labour literature as the unit of analysis for my exploration of labour conditions in the VI industry. Political economy, emotional capitalism, and affect theory frameworks guide this analysis. I argue that the labour ecosystem surrounding VIs represents concerning future trends in labour exploitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".