Story recognition, “plot-gene” access and retrieval from the “mental sketchpad”: case-study (Yvan Attal’s film “Ils sont partout” and its ancient counterparts)
Bibliographic record
Abstract
The article presents a case study which exemplifies the cognitive process of “plot-gene”1 recognition and its retrieval from the memory- ’s “mental sketchpad”.2 Specifically, the article analyzes four cinematic representations, in which the same “plot-gene” – a pejorative take on the birth of Jesus – is found: the movie by Yvan Attal titled “Ils sont partout” (available on Neflix as “They are everywhere”), the movie “Life of Brian” by “Monty Python”, the movie “Jesus of Montreal” by Denys Arcand, as well as several anti-religious sketches by the British comedian Rowan Atkinson. These movies are considered in the article at the level of their cinematic plots and narrative schema, as well as at the ideological level. The article provides comparative data for the identification of the same plot-gene in different contexts that date back to antiquity and can be found cross-culturally worldwide. The article utilizes the motif-compass method developed by the Russian scholar Olga Freidenberg. In conclusion, the article postulates the hypothesis that the plot-gene under investigation always resurges due to the specific ideological reasons (i.e., resistance strategy towards Anti-Semitism, expression of anti-clerical sentiments).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".