<i>Crash Landing</i> on the Philippines: Transnational Korean Drama and Internet Infrastructural Desires
Bibliographic record
Abstract
In 2020, the Philippines’ largest mobile network provider Smart Communications launched a year-long campaign with South Korean actor Hyun Bin, who gained high popularity among Filipino audiences through the Korean TV drama Crash Landing on You (TvN 2019-2020) aired through Netflix. This article analyzes media texts, government plans, corporate narratives, and infrastructure data to examines two ways that transnational media, such as K-dramas, function as cultural interfaces to disseminate and operationalize the infrastructural desires in the Philippines. First, Philippine internet service providers (ISPs) co-opt Netflix’s language of internet speed as a criterion of infrastructural quality, trying to secure the country’s public and economic recognition in Southeast Asia. Second, through the collaboration, local ISPs successfully translate K-drama’s cultural power into a public campaign presenting high-speed internet as not merely desirable, but a predestined future for digital consumers and the developing nation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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 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".