A Mode of Agitation between <i>Verfremdungseffekt</i> and Empathy: Breaking the Fourth Wall in Craig Gillespie’s <i>I, Tonya</i>
Bibliographic record
Abstract
Le présent article analyse le film Moi, Tonya (Craig Gillespie, 2018) en se penchant particulièrement sur la technique théâtrale d’adresse directe à la caméra. Cette méthode crée un mode d’agitation qui fluctue entre l’effet de distanciation de Brecht et l’empathie. L’utilisation de gros plans faciaux, de zooms et de travellings dans des espaces intimes suscite des émotions chez l’auditoire à travers un récit alternatif de Tonya Harding. Cependant, les scènes sapent continuellement la confiance ainsi développée à travers le témoignage contradictoire et la figure de Tonya désignant le téléspectateur comme étant son « agresseur ». Moi, Tonya s’attaque de façon agressive aux répercussions sociales et politiques à plus grande échelle de la violence conjugale dans les ménages de faible statut socioéconomique aux États-Unis, la consommation médiatique dénuée de toute critique et les structures capitalistes.
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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 | 0.005 |
| 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 | 0.019 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".