<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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".