Narratological Approaches to Multimodal Cross-Cultural Comparisons of Global TV Formats
Bibliographic record
Abstract
This article cross-culturally compares different versions of the Quebec sitcom/sketch comedy television series Un Gars, Une Fille (1997-2002) by examining the various gender roles and family conflict management strategies in a scene in which the heterosexual couple visits the male character’s mother-in-law. The article summarizes similarities and differences in the narrative structure, sequencing and content of several format adaptations by compiling computer-generated quantitative and qualitative data on the length of segments. To accomplish this, I have used the annotation function of Adobe Premiere, and visualized the findings using Microsoft Excel bar graphs and tables. This study applies a multimodal methodology to reveal the textual organization of scenes, shots and sequences which guide viewers toward culturally proxemic interpretations. This article discusses the benefits of applying the notion of discursive proximity suggested by Uribe-Jongbloed and Espinosa-Medina (2014) to gain a more comprehensive and complex understanding of the multimodal nature of cross-cultural comparison of global television format adaptations.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".