Reclaiming French Detroit: Addressing an Historiographical Gap in Québec’s History
Bibliographic record
Abstract
This article addresses the longstanding absence of French Detroit – the French-Canadian communities that developed on both sides of the Detroit River – from Québec historiography. Despite enduring migration, kinship, and religious ties to the St. Lawrence Valley, French Detroit has remained largely invisible in Québec’s historical narrative. The article argues that this omission results from a combination of nationalist historiographical priorities, institutional silences, and a narrow geographical focus. Through case studies of migrants like Étienne Crête and Frédéric Gagnier, the author highlights the persistence of French-Canadian mobility and settlement in the Great Lakes region well into the nineteenth century. The article also examines why nineteenth-century observers such as Bouchette and Tocqueville, and early Québec-trained historians, failed to integrate this history into their narratives. The piece concludes by calling for a continental approach that redefines Québec’s historical geography to include the experiences of French Canadians beyond the province’s current borders. Reclaiming French Detroit not only enriches our understanding of French North America but also challenges the historiographical boundaries that have shaped Québec’s past.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".