Infernal Journeys in Marie-Célie Agnant’s <i>Femmes au temps des carnassiers</i>
Bibliographic record
Abstract
The emphasis on testimony in the oeuvre of Haitian-Québécois author Marie-Célie Agnant attests to her recognition of the vital task of bringing truth to light. Indeed, most of Agnant’s works take as their focal point the far-reaching consequences of the Duvalier regime, underscoring in particular the crucial importance of giving voice to the terrifying events that occurred throughout this period. In the richly layered Femmes au temps des carnassiers (2015), a number of Haitian women recount their experience of political violence and testify to the “unspeakable” atrocities that turned Haiti into a “terre gorgée de sang” (Agnant 2015, 21). While this text addresses the complex relationships between history, memory, silence, and voice, it does so by emphatically equating the Duvaliers with the demonic and women’s trauma with a form of hell. Agnant’s deployment of the trope of hell characterizes her female protagonists’ trajectories as a grueling journey into an infernal realm with no guarantee of return. At the same time, however, the narrative strongly suggests that bearing witness, especially through art, can potentially play a significant role in bringing about healing after an unwanted descent into the underworld. Le thème récurrent du témoignage dans l’œuvre de l’écrivaine haïtienne-québécoise Marie-Célie Agnant souligne l’importance qu’elle attache à la nécessité de mettre en lumière la vérité sur des événements historiques troublants. En effet, la plupart des textes d’Agnant ont comme sujet central les conséquences du régime des Duvalier et insiste en particulier sur les événements terrifiants qui ont eu lieu pendant cette période. Dans le roman Femmes au temps des carnassiers (2015), plusieurs générations de femmes haïtiennes victimes de la violence politique racontent les atrocités “indicibles” qui ont transformé Haïti en une “terre gorgée de sang” (Agnant 2015, 21). Bien que ce texte traite des thèmes de prédilection d’Agnant - les rapports complexes entre l’histoire, la mémoire, le silence et la voix - il le fait en créant un parallèle frappant entre l’enfer et les traumatismes subis par les femmes. L’emploi du trope de l’enfer sert ainsi à présenter les Duvalier comme une incarnation du diable et les trajectoires des personnages femmes comme un voyage douloureux sans fin dans un monde infernal. En même temps, cependant, le récit suggère fortement que l’acte de témoigner, surtout à travers l’art, peut potentiellement jouer un rôle significatif dans le processus de la guérison après une descente aux enfers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".