Representing the Twenty-First Century Migrant Experience: Adam and Fleutiaux’s Problematic Empathy
Bibliographic record
Abstract
In recent years, a trend in French literature has emerged among non-migrant French authors. In her 2018 study, The Migrant Canon in Twenty-First-Century France, Sabo describes this trend as “the emergence of French authors who write about migration” (27). Similarly, Louviot argued that “the drama of migrants dying on Europe’s doorstep has inspired many […] French writers with no postcolonial or (im)migrant background” (6). This article—which focuses on two texts, À l’abri de rien by Olivier Adam (2007) and Destiny by Pierrette Fleutiaux (2016)—examines how non-migrant French authors have attempted to give a voice to illegal migrants in their recent literary works. Each work recounts the story of a French woman who attempts to help one or several migrants as they navigate horrid living conditions (in a Calais-like city in À l’abri de rien and in Paris in Destiny), suffer mental and physical breakdowns, and face French authorities. This study demonstrates that there is an inherent ambivalence at the heart of how these two non-migrant French authors have attempted to voice the plight of today’s illegal migrants in France. While Adam and Fleutiaux’s texts aim to foster empathy toward migrants, they also feature complex altruistic motives that are far from selfless. Adam and Fleutiaux strive to humanize migrants and their trajectories by creating an empathic discourse of care. However, migrant characters are also portrayed as passive objects of fascination becoming pawn-like figures in the lives of the two white female protagonists. The article questions these characters’ altruism by analyzing how their own mental states overpower their empathic drives, thus bringing to light the questionable reasons why these two women become consumed by the need to help migrants. Ultimately, these considerations help build a critique of the problematic empathy Adam and Fleutiaux have constructed and its ethical ramifications.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.037 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".