French-Speaking Minorities in Ontario and New Brunswick: Surviving or Thriving?
Bibliographic record
Abstract
When the Royal Commission on Bilingualism and Biculturalism recommended that both Ontario and New Brunswick become officially bilingual in 1967 to prevent inevitable linguistic assimilation, only the latter did so. My research question focuses on the effects of Ontario's decision. Was the Commission right in maintaining that official bilingualism was necessary? Has Ontario created a language regime that is able to prevent assimilation without becoming fully bilingual? I aim to explore whether Ontario's decision not to adopt bilingualism has led to increased assimilation for Franco-Ontarians as compared to Acadians in New Brunswick. To do so, I examine the Speeches from the Throne and legislation in both provinces from 1967 to 1985 to investigate the level of commitment in each province to preventing assimilation. I use reports from both provinces in 1985 to determine whether there was a disparity in the level of access to French-language services between the two provinces. To study the possible effects of any disparities, I examine census data to measure linguistic assimilation rates in both provinces from 1971 to 2011. I expect to find that Ontario had a lower level of commitment to its French-speaking citizens' needs than New Brunswick, and that this has translated into higher rates of assimilation for Franco-Ontarians.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".