Facing algorithmic uncertainty: cross-platform labor strategies of small and medium-sized video creators on mainstream platforms
Bibliographic record
Abstract
With the rise of the creator economy, more content creators now distribute across TikTok, YouTube, and Instagram to expand influence and diversify income. Yet these platforms differ in style, visibility, and monetization, posing new challenges and uncertainties for creators work. This study examines small-to-medium-sized digital creators to explore how they adapt their labor strategies across platforms and the key factors driving these adjustments. Through semi-structured in-depth interviews with approximately 15 creators, the research identifies algorithmic uncertainty, the accumulation of emotional labor, and the volatility of income models as primary drivers prompting creators to modify their platform strategies. This research deepens understanding of how small-to-medium creators adapt their content production and operational approaches when navigating multiple platform rules. It not only enriches theoretical insights into labor practices in the platform economy but also provides valuable references for platform design and policy formulation aimed at supporting creators' survival and development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".