Controverse Blitzchung : étude de l’activisme fan au sein d’une communauté de joueurs de jeux vidéo
Bibliographic record
Abstract
Cet article s’intéresse à la manière dont l’activisme fan de certains leadeurs de la communauté des joueurs a participé à la construction du discours et influencé le mouvement de contestation dans le contexte de la « controverse Blitzchung ». Lors de cet événement, débuté le 6 octobre 2019, Blitzchung, un joueur du jeu vidéo Hearthstone, a scandé un message pro-Hong Kong en direct suite à sa victoire lors d’un tournoi. Deux jours plus tard, l’éditeur Blizzard l’a sanctionné, ce qui a entraîné plusieurs actions en ligne et sur le terrain en guise de protestation pour défendre le joueur et dénoncer le pouvoir de l’État chinois. Nous démontrons la manière dont l’activisme fan a participé à la construction du discours et a influencé le mouvement de protestation lors des événements entourant cette polémique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".