East Asian Perspective in Transmedia Storytelling | Korean Webtoonist Yoon Tae Ho: History, Webtoon Industry, and Transmedia Storytelling (Feature)
Bibliographic record
Abstract
At the Asian Transmedia Storytelling in the Age of Digital Media Conference held in Vancouver, Canada, June 8–9, 2018, webtoonist Yoon Tae Ho as a keynote speaker shared several interesting and important inside stories people would not otherwise hear easily. He also provided his experience with, ideas about, and vision for transmedia storytelling during in-depth interviews with me, the organizer of the conference. I divide this article into two major sections—Yoon’s keynote speech in the first part and the interview in the second part—to give readers engaging and interesting perspectives on webtoons and transmedia storytelling. I organized his talk into several major subcategories based on key dimensions. I expect that this kind of unusual documentation of this famous webtoonist will shed light on our discussions about Korean webtoons and their transmedia storytelling prospects
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".