Transnational media and English spread in the Expanding Circle: Hollywood’s predominance, language accommodation, and English as an additional language in cinema, television, and video on demand
Bibliographic record
Abstract
Abstract This paper explores the relationship between transnational media and the dramatic increase in English language users and uses within Kachru’s (1990. World Englishes and applied linguistics. World Englishes 9(1). 3–20) Expanding Circle. Traditionally, English has been considered a Foreign Language in the third and demographically largest sphere of Kachru’s (1990. World Englishes and applied linguistics. World Englishes 9(1). 3–20) World Englishes framework. Yet in recent decades, the language has spread significantly within speech communities in e.g., continental Europe, South America, and the Middle East. In contemporary Expanding Circle contexts, English has gained international and local uses, for interactions with individuals from abroad and fellow speech community members. Thus, English has evolved beyond its single, traditional role to acquire the added functions of Lingua Franca and Additional Language. This study examines these changing roles in connection with transnational media’s development over the last century, given the leading industry position of L1-English Hollywood. The paper reviews the transnational history of cinematic film, television programming, and video streaming on-demand, with their evolving top-down language policies and bottom-up viewer practices. The European context of Germany illustrates how English use within these domains over time reflects changing proficiencies and roles for the language.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".