"And I think to myself - what a Marvel-ous world" an examination of Marvel Studios' influence and role in the franchising of contemporary superhero films
Bibliographic record
Abstract
character's evolution, as did various Superman television productions a few decades later in the 1950s and film in the 1980s.3 Superhero comics, a transmedia phenomenon from the start, anticipated the expansive and immersive world building essential to what has been called convergence culture.4 The works of media scholar Henry Jenkins are crucial in comprehending developments in convergence culture and transmedia storytelling that have become prominent over that past two decades.The main development concerns the integration of multiple texts to create a narrative so large, encompassing and constantly growing, that it cannot be contained within a single medium.5 In convergence culture, each platform in a transmedia franchise allows for the introduction of new parts into the larger narrative.6 A narrative's world can be expanded and explored across multiple entertainment platforms with any given product providing a point of entry into the franchise as a whole.7 The Wachowskis' Matrix franchise exemplifies the degree to which a transmedia franchise, a narrative or property can exist across multiple platforms and formats.8 The possibilities are endless as to how an audience can consume a transmedia narrative, which imposes special challenges on those who wish to analyze such narratives.Superhero comics provide the perfect medium for convergence culture and transmedia franchising.Indeed, today's convergence culture can be traced back to the comics industry in the 1930s and 1940s.Since their inception, DC Comics and Marvel Comics have treated all of their titles as interconnected; characters move across different 3 Ibid., 304. 4 Ibid.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".