From the Periphery to the Center: Evolution of the Spatial Distribution of the French-Speaking Community in New Brunswick (Canada)
Bibliographic record
Abstract
Linguistic issues are an integral part of the political debate in Canada, particularly for the French-speaking minorities in the English-speaking provinces. Urbanization in the Anglophone-dominated urban areas of the more peripheral French-Speaking communities has considerably modified the linguistic map of the country. In New Brunswick, the only official bilingual province of Canada, French-speaking Acadians have been able to resist to linguistic assimilation, thanks to a particular form of territorial appropriation. Their migration from the rural areas (the « periphery ») to the cities (the « center »), as a result of rapid urbanization since the 1960s, have deeply modified the francophone space in New Brunswick. Although the majority of Acadians is still concentrated in the northern part of the province (the « periphery »), those who migrated have mostly moved to the city of Moncton (the « center »). These two cores, one regional, the other urban, direct the flow of support for the creation of institutions. This study is part of a series of spatial and temporal analyses on the evolution of the Francophone population during the four last decades, using Geographic Information Systems (GIS). The results present a global overview of the evolution of the location of the Acadian Francophone population in New Brunswick and will help us understand the main challenges created by these spatial transformations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".