WHERE MY AI APPS AT? A HISTORIOGRAPHIC APPROACH TO ANALYZING PLATFORM TOOLS
Bibliographic record
Abstract
The popular short-form video app TikTok is mainly discussed as a discrete app or in relation to its parent company ByteDance. This view neglects how TikTok and other ByteDance apps maintain and advance ByteDance’s highly complex app ecosystem. This paper, therefore, positions ByteDance-owned apps as both apps and “platform tools.” TikTok allows end-users to watch videos, allows creators to make and distribute content, advertisers to endorse products, and developers to build app features. As a platform tool, TikTok is a software-based resource that mediates “platformization,” extending TikTok’s economic, infrastructural, and governmental data-centric logic within and beyond ByteDance’s app ecosystem. Increasingly, ByteDance’s platform tools rely heavily on AI technology because of ByteDance’s early investments in AI technology and the growing interest such tools within the cultural industries. We survey ByteDance’s AI-powered platform tools alongside non-AI ones using systematic financial and infrastructural analysis, uncovering how ByteDance’s platform tools expand ByteDance as a “multi-sided,” “multi-layered,” and “multi-situated” platform. Platform tools, thus, facilitate growth along these three dimensions by encouraging platform dependence; interoperability and interdependence within ByteDance’s app ecosystem; and platformization, including “parallel platformization.” Our empirical work ultimately shows how ByteDance uses platform tools to accrue and operationalize infrastructural and economic power, and how apps have moved from discrete objects to interconnected clusters of platform tools.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".