The globalization of TikTok: Strategies, governance and geopolitics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it