TikTok’s tools: The politics of platform tools for cultural production
Bibliographic record
Abstract
The rapid economic and infrastructural expansion of shortform video app TikTok can be attributed to its emphasis on software resources facilitating cultural production. Such tools contribute to the process of ‘platformization,’ the extension of platform business models and governance regimes within and outside the cultural sector, and ‘infrastructuralization,’ the increasing involvement of platform companies in providing critical systems and services. Platform scholars have argued that platformization and infrastructuralization lead to platform dependence. Increasingly, platform tools, being infrastructurally integrated with platform companies, drive these processes. Using the boundary resources framework, this article conducts a platform historiography of TikTok by mapping the expansion and evolution of its toolsets. In doing so, this paper makes two contributions to platform scholarship. The first is both conceptual and methodological: we classify platform tools and outline an interdisciplinary approach to systematically plot changes to them, remaining attentive to their dynamic, relational, and contextual nature. Second, our empirical work uncovers how first-party platform tools are developed and managed to become increasingly comprehensive, centralized, and integrated. The paper concludes with a call for future research on platform tool governance to understand how platform companies encourage platform dependence across societal sectors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".