Where We Go One, We Go All: QAnon, Networked Individualism, and the Dark Side of Participatory (Fan) Culture
Bibliographic record
Abstract
Abstract Participation in online spaces has afforded new fan cultures (Baym, Burnett 2009; Jenkins 2018) and enabled new communities of networked individuals (Rainie, Wellman 2012; Burgess, Jones 2020). Online participation also generates participatory cultures, which allow audiences unprecedented opportunity to connect with each other and with the media they share. However, it has also generated some decidedly anti-social and anti-democratic movements, such as QAnon (Amarasingam, Argentino 2020). In this commentary, we argue that QAnon can be thought of as a participatory fan culture gone awry. Using QAnon’s entry into mainstream culture in 2020 as a case study, we explore the darker implications of online participatory culture, including misinformation, conspiratorial- thinking, and an undermining of shared realities. Lastly, we propose that these issues are made more explicit and difficult to attend to in a media sphere characterized by dominant neo-liberal corporate control of participatory media, and digital dualism.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".