La vengeance chez Frédéric Marcelin et Sony Labou Tansi : La vengeance de Mama (1902, 1974) et La Vie et demie (1979)
Bibliographic record
Abstract
Nearly 70 years apart, two francophone authors, the Haitian Frédéric Marcelin (1848-1917) and the Congolese Sony Labou Tansi (1947-1995), published novels whose plot either entirely or partially calls upon the theme of vengeance, each featuring the same principal elements: women, dictators, champagne, poison, seduction and sex. In Marcelin’s La vengeance de Mama (1902, 1974), Zulma Corneille, nicknamed Mama, avenges the death of her fiancé, Épaminondas Labasterre, by making his murderer Télémaque drink poisoned champagne during an amourous rendez-vous; in Sony Labou Tansi’s La Vie et demie (1979), after seeing her entire family murdered by the Guide Providientiel de la Katamalanasie, Chaïdana, the daughter of the antagonist Martial, likewise uses poisoned champagne to kill off a number of the totalitarian country’s officials after having offered herself to them in the novel’s eponymous hotel. The goal of this article is to investigate what must be fortuitous similarities between these two novels, as it is unlikely that Labou Tansi would have read Marcelin. It aims to analyse the way in which vengeance is employed in the novels, notably the use of ruses and seduction as a means of enticing the victims.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".