Subtitling in the Streaming Era: A Comparative Analysis of Strategies Used to Translate Cultural References into Arabic
Bibliographic record
Abstract
In the wake of the COVID-19 pandemic and the subsequent frequent lockdowns, streaming platforms have become an essential part of people’s media consumption. Translation has played a key role in the rapid growth of these streaming services by allowing global access to different translated versions of their content. While the impact of these streaming services on the reconstruction of media production has been a reoccurring topic in media studies, their impact on translation is still relatively underresearched. With that in mind, this article aims to identify current translation tendencies by examining the translation strategies commonly used in subtitling cultural references (CRs) into Arabic on three of the primary streaming platforms (Netflix, OSN+ and Prime Video) in the Middle East and North Africa (MENA). This is particularly significant not only because most studies on cultural references focus on European languages and contexts, but also because the few studies examining Arabic subtitles are mainly focused on subtitles available on DVDs (Alfaify, 2020), fansubbing websites (Abdelaal, 2019) and satellite television (Thawabteh, 2014). Additionally—and contrary to the common practice, which tends to ignore the complex multimodal nature of the cultural references—this article examines both verbal and crossmodal cultural references and the patterns of rendering them in professional subtitles across the investigated streaming platforms.
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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 | 0.002 | 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 | 0.000 | 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 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".