Bibliographic record
Abstract
Imagining Transmedia is an ambitious proj ect involving interdisciplinary bridge-building between academic research and industry practice.Not surprisingly, much ink is spilled here on definitional questions, as participants explain how and why they use the term "transmedia."Readers should pay close attention to how each writer defines transmedia's bound aries or maps its relationship to other related terms (such as "cross-media," "cross-platform," "intertextuality," "multimodality," and "paratexts")."Transmedia" literally means "across media," and it denotes a structured relationship of texts and practices that cut across multiple media.Elsewhere, Benjamin W. L. Derhy Kurtz and Mélanie Bourdaa (2017) use the term "transtexts" to refer to both commercial and fan works that interact together within a transmedia system, while others argue that there is no such thing as a "transmedia text" since the texts gain meaning only relationally.From that perspective, "transmedia" refers to a production strategy or an interpretive practice.Given how expansive some theories of media are, we should not be surprised that writers differ over what constitutes a medium (McLuhan, 1964;Peters, 2015)."Transmedia" is less a noun than an adjective: it needs to modify something.In my own recent work (Jenkins, 2017), I talk about "storytelling," "learning," and "activism" as often overlapping logics describing the general aims of a par tic u lar transmedia proj ect.How could we have a stable or even coherent definition of "transmedia," given the fact that we are discussing emerging and evolving practices within a media landscape that is itself always being reconfigured?These ambiguities have surrounded the concept from the start.I should know.I am often called the " father of transmedia."I duck this label.I did not coin the term (Marsha Kinder may have, in 1993).
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.039 | 0.022 |
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".