Transmedia, Translation and Adaptation: Parallel Universes or Complex System?
Bibliographic record
Abstract
This article explores the relationship between transmedia narratives, translation, and adaptation, and exposes why one can be lead to believe that these disciplines have very little in common. It first gives a definition of complexity and transmedia narratives, and explains the difference between transmedia, crossmedia, and multimedia. It then describes the types of transmedia projects proposed by Christy Dena (2011, n.p.) and illustrates them with the examples of two cult series’ narrative universes, that is, Twin Peaks and Skam. Transmedia stories, which spread on multiple platforms and involve audience participation, are considered by transmedia theorists to be part of story worlds (story universes, or “storyverses”). Yet, most of these narratives can be considered as adaptations, or as multimodal, intersemiotic translations. Despite the evident relationship between translation, adaptation and transmedia, adaptation is reduced to a mere media transfer, and translation is mostly referred to as an interlinguistic operation in recent academic conversations around transmedia and participatory culture. This article examines how the emergence of transmedia narratives illuminates the fact that adaptation and translation must step into the study of contemporary transmedial landscape, and how transmedia, translation and adaptation could gain from it.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.087 |
| Scholarly communication | 0.020 | 0.036 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".