Making the Invisible Visible to Our Students: Reading Marie-Célie Agnant Within a Social-Justice-Oriented French Curriculum
Bibliographic record
Abstract
This article examines the works of Haitian-Québécois writer Marie-Célie Agnant within the framework of a social justice-oriented French curriculum. Situating Agnant’s contributions within the evolving discourse on critical pedagogy in world language education, it highlights Agnant’s engagement with themes of power and oppression within and beyond the Haitian diaspora context, demonstrating how her texts reveal systemic injustices tied to gender, race, immigration, and linguistic identity. The analysis extends beyond Agnant’s well-studied adult novels to her young adult literature and short stories, emphasizing their pedagogical potential. Vingt petits pas vers Maria (2001) is examined for its critique of linguistic and social hierarchies, while Alexis d’Haïti (1999) and Alexis, fils de Raphaël (2000) illuminate the challenges faced by Haitian migrants in North America. The article also explores Agnant’s later short fiction, which demonstrates “thick solidarity” (Liu and Shange 2018) with marginalized communities beyond Haiti, notably in Nouvelles d’ici, d’ailleurs et de là-bas (2017). By integrating Agnant’s texts into French classrooms, educators can foster critical engagement with issues of power and privilege while enhancing students’ linguistic and analytical skills. Agnant’s works serve as a powerful tool for teaching both language and social justice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".