Language Ecology and Shift at Baawating, 1600-1971
Bibliographic record
Abstract
Research focused on the macro-trends in Canadian language policy (LP) has largely focused on two broad trajectories: (a) the processes of accommodation of Anglophone and Francophone communities (including the limitations of Canada's policy of bilingualism for French-speaking or official-language minority communities) (Martel & Pquet, 2010; Morris, 2010; Cardinal, 2015); and (b) the ongoing exclusion of The Other (i.e. "immigrant" and Indigenous communities) within Canadas existing LP framework (Haque, 2012; Haque & Patrick, 2015; Patrick, 2018). This research turns its focus to the place of language in the state formation processes of Canada that preceded its "Bilingualism within a multicultural framework," and its place in settler/Indigenous relations and processes of colonization. Building on the paradigm of the Anishinaabe Seven Fires prophecies and a framework that emphasizes the interplay of language practices, beliefs and management in a social ecology, this work offers a case study of the specific experiences of Indigenous peoples in the communities surrounding Baawating (at the junction of Lake Superior and Lake Huron) to exemplify: (a) how Indigenous individuals adjusted their language choices in response to institutional language policy? (b) How Canadian Indian Policy more generally affected those language choices? (c) How these choices impacted relations between Indigenous and settler peoples? And (d) how local language practice, belief, and management processes have been impacted by the surrounding socio-economic, physical, political, and cultural environments? The study uses a mixed-methods approach that combines content analysis of language policy documents, historical records, demographic data and interviews of local Indigenous residents on their experiences of language choice and use to triangulate the interplay between macro-level LP, ideologies of language, and language shift. The research demonstrates the interconnection of LP with social, economic, political and technological domains and their corresponding influence on the linguistic choices available to Indigenous peoples, which precipitated large-scale language shift. Furthermore, it illuminates how language has been used to stand-in for race in the construction of idealized national subjects within a liberal order since at least the early twentieth century in Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".