Bibliographic record
Abstract
This article examines the transformative effects of platforms on cultural production through an analysis of the LINE “super app.” Super apps are apps that do-everything; mega-platforms unto themselves. They are particularly prevalent in East Asia. Like China’s WeChat or South Korea’s KakaoTalk, Japan’s LINE has evolved from a single purpose chat app to the do-everything platform for everyday cultural and economic activities. It is also the very reason for the global proliferation of stickers or large-size emoji in other chat apps, from Apple’s iMessage to Facebook’s Messenger to Tencent’s WeChat. This article offers a close examination of LINE to highlight and theorize the process of the “platformization of cultural production.” To do so, it traces Japan’s longer history of platforms going back to the i-mode mobile platform launched in 1999, and examines LINE’s regionally specific sticker-oriented strategies in East Asia. With a focus on the entrepreneurial work of sticker designers as cultural producers, this article also mobilizes LINE to both highlight the specificities of this platform and contest the excessive attention paid to platforms from Silicon Valley, or, at best, their Chinese counterparts. LINE and the regional convergences of super apps in East Asia are a potent reminder of the need to analyze platforms outside of the bi-polar hegemony of the United States versus Chinese tech world—which increasingly frames journalistic discourse and academic research—and of the need to attend to the historical and regional particularities of platforms and their cultural impacts.
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".