Televisuality on a Global Scale: Netflix’s Local-Language Strategy
Bibliographic record
Abstract
This article focuses on Netflix’s local-language strategy, the context leading up to it, and the extent to which transnationality, in this particular case, becomes televisual in John Caldwell’s sense. I argue that Netflix has developed a different business model for transnational TV formats through this strategy. For that, I use the Netflix original and exclusive series Criminal (Field Smith & Kay, 2019–present-a–d) as a case study and show that its production context triggers a specific visual response due to Netflix’s economic and legal obligations in Europe. Building on the “transnational TV format trading system” approach of Jean K. Chalaby, this case study highlights how the affordances of multi-country video-on-demand providers like Netflix allow for the successful international franchising strategy in linear television to be conducted internally and simultaneously. Specifically, it shows that fictional TV series no longer need to be developed for a national broadcaster before reaching international markets because multi-country video-on-demand providers do not require various national intermediaries to distribute and stream TV series in different markets. The adaptation process can also be bypassed entirely if the decision to localize a programme into multiple versions is made before production starts. As a result, companies like Netflix can produce several local variations of TV content without running into as many barriers as national broadcasters. From there, I further argue using Mareike Jenner’s “grammar of transnationalism” that the impact of production and distribution processes on the visual treatment of Criminal leads to style excess at the interface level and stylistic scarcity at the aesthetic level.
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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 | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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, 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".