The globalization of TikTok: Strategies, governance and geopolitics
Bibliographic record
Abstract
This article examines the rise of TikTok in three aspects: globalization strategies, data and content policies, and geopolitical implications. Instead of focusing on app features and uses within the platform proper, we situate and critically analyse TikTok as a platform business in a global media policy and governance context. We first unpack TikTok’s platformization process, tracing how TikTok gradually diversifies its business models and platform affordances to serve multisided markets. To understand TikTok’s platform governance, we systematically analyse and compare its data and content policies for different regions. Crucial to its global expansion, we then look at TikTok’s lobbying efforts to maintain government relations and corporate responses after facing multiple regulatory probing by various national governments. TikTok’s case epitomizes problems and challenges faced by a slew of globalizing Chinese digital platforms in increasingly contested geopolitics that cut across the chasms and fault lines between the rise of China and India as emergent powers in the US-dominated global platform ecosystem.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".