Platformization of Korean Internet portals toward mega-platforms: A historical approach
Bibliographic record
Abstract
This paper documents the evolution of Korea’s digital platforms. By using a historical approach in tandem with platformization — helpful in determining the causes behind the changing processes of new technologies — we examine the advancement of digital platforms. The digital platform era can be divided into three significant periods based on major technical advancements and corporate transformations, including the early construction of ICT infrastructure between the mid-1990s and early 2000s; the early platformization period of Internet portals amidst the smartphone revolution between the mid-2000s and mid-2010s; and the duopoly market of Naver and Kakao from the mid-2010s, after the merge of Daum and Kakao, to the present. Multiple causes led to the advent of digital platforms, both in terms of technologies and systems. Power relations developed between several major players, such as the government, corporations, and global forces. This work ultimately describes the relationship between sociocultural transitions and accompanying structural changes in digital platforms and relevant policies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".