From Comic-Con to Amazon: Fan conventions and digital platforms
Bibliographic record
Abstract
San Diego Comic-Con is North America's premiere fan convention and a key site for mediating between media industries and fandom. In 2020, the COVID-19 pandemic forced Comic-Con to abruptly move its programming onto an array of digital platforms in an apparent "platformization" of the con. Informed by research on fan conventions, media industries, and the platformization of cultural production, this analysis of the online convention argues that Comic-Con was primed for platformization because it is already platform-like. Conventions organize markets, infrastructures, and governance to bring together attendees, media industries, and other "complementors." Moreover, platform logics were already shaping the convention pre-pandemic in the form of experiential marketing and brand activations designed to capture attendee data. Rather than a radical break, the Comic-Con@Home online convention and in particular Amazon's Virtual-Con activation are part of a longer process of reconfiguring the relationships between fan conventions, cultural producers, and platforms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".