Televisuality on a Global Scale: Netflix’s Local-Language Strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <em>Criminal</em> (Field Smith &amp; 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 <em>Criminal</em> leads to style excess at the interface level and stylistic scarcity at the aesthetic level.
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.
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 | 0.001 | 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) | 0.000 | 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 it