Critical Interpretations of Global-Local Co-Productions in Subscription Video-on-Demand Platforms: A Case Study of Netflix’s <i>YG Future Strategy Office</i>
Bibliographic record
Abstract
This study examines the dynamics of co-production between a global subscription video-on-demand (SVOD) platform and a local producer. Based on a case study of “YG Future Strategy Office” co-produced by YG Entertainment and Netflix, it examines how various expectations of both companies are embedded in this series. On one hand, YG considers co-production as a means of promoting its artists for the global market which otherwise cannot be produced through pre-existing broadcasters. On the other hand, Netflix expects such co-productions to target the Asian market so that it can respond to the entry of incumbent media moguls into the SVOD market. While such co-productions seem to benefit both global platforms and local producers on the surface, however, this relationship may result in deteriorating the position of local actors as potential subcontractors considering the importance of distribution in the mediascape.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".