Les relectures empathiques (im)possibles d’Un alligator nommé Rosa de Marie-Célie Agnant
Bibliographic record
Abstract
The aim of the present paper is to answer the question whether it is possible to reread the book Un alligator nommé Rosa (2007) by Marie-Célie Agnant, a Quebec writer of Haitian origins, through the lenses of empathy and of what A. Gefen (2017) names the “empathetic participation” of the reader. The book tells the story of two victims of Rosa Bosquet (based on the real person), the cruel right-hand woman of former Haitian president F. Duvallier, who decide to punish Rosa, now old, paralyzed, mute, and entirely defenseless. The paper is divided into three parts, analyzing the three main protagonists and their possible empathetic reception by the reader. It turns out that the narrative strategies used by Agnant and the changes of role between executioner and victim, provoke that the empathy is frequently blocked. It does not mean however that the empathetic rereading of the book is vain: it makes the reader reflect on the nature of Good and Evil and encourages them to “repair the world”.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".