Subjects of Interpretation: Second Language Acquisition by Jesuit Missionaries among the Northern Ojibwa, 1842–1880
Bibliographic record
Abstract
This article focuses on second-language learning and “linguicide” in Upper Canada between 1843 and 1877. From the small group of Jesuits that made up the ranks of the Society of Jesus’ new missions to Canada in the post-suppression era, it was Jean Pierre Choné, Joseph Hanipaux, Nicholas Frémiot, and Dominique du Ranquet, August Kohler, Nicolas Point, and Joseph Jennesseaux that first learned Algonquin languages in order to proselytize to the Northern Ojibwa populations at the Upper Canada. The Upper Canada mission, led by superior Pierre Chazelle, re-established some of the Society of Jesus’ older Aboriginal missions, and expanded their evangelical territory north and west along Lake Huron and Lake Superior. Important stations were built among the Ojibwa at Wikwemikong on Manitoulin Island in 1844, in Sault Sainte Marie in 1846, and along the Pigeon and Kamanistikwa Rivers, near Fort William, in 1848. This paper examines why the new Jesuits were motivated to learn the languages spoken at their Aboriginal missions in the nineteenth century and simultaneously investigates how the massive and unexpected psychological challenges of the 1800s, including anti-Catholicism, British rule, mass immigration, and formidable industrial development in Upper Canada, supported or discouraged the Jesuits’ language acquisition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".