Beyond National Narratives: A Relational Method for Diasporic Literatures Grounded in Francophone Canada
Bibliographic record
Abstract
This article asks how to read African diasporic writing in French in Canada without forcing it into national boxes. It reframes the old idea that “one people, one territory, one language” should define literature as a habit that can hide what texts actually do on the page. I propose a simple toolkit of six questions to guide analysis: How does the text create contact across difference? How does the city shape scenes and pace? How do past, present, and hoped-for futures overlap? To whom does the voice speak, and how does it hold more than one audience? How do spoken forms like a proverb or a chant become a written rhythm? How do civic and religious worlds share a scene? Three readings model the approach: Didier Leclair writes the migratory city through thresholds, transit, and offices; Monia Mazigh builds ethical address across civic and faith publics; Guy Armel Bayegnak turns cadence, meaningful objects, and mixed codes into structure. A synthesis shows translation on the page working as a method, and “public-making” as a measure of literary form in minor settings. The article offers a clear, portable way to name how texts build relations beyond national narratives. Although grounded in francophone Canada, the method is designed to travel and can inform the analysis of diasporic and minor literatures worldwide.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".