French and Canadian French, Are They Really Different?
Bibliographic record
Abstract
My project would aim at observing the linguistic differences between French spoken in Québec and France. Although English is the most spoken language in Canada, Quebec is known for being a French speaking province since the 17th century, due to French colonists. French from Québec and French from France are often said to be different in terms of accent and idioms. However, they are much more distinct, especially when it comes to the pronunciation and even the linguistic structures of certain words, phrases and sentences. Therefore, I would like to explain what makes them so different. How has English influenced the way people speak in Québec? What are the noticeable phonetic, phonological and syntactical differences? In other words, how do they sound and are grammatically constructed differently? Although French from Québec can be understood by French people and vice versa, it is important to recall that it might be more difficult for English speakers learning French to understand Québec French. It is linguistically and culturally relevant to observe these differences to recognize that despite their differences, both types of French are legitimate and deserve a specific attention. The main distinction that could be found between those two types of French is that French from Québec is characterized by a different type of vocabulary, and a different pronunciation due to the English influence. Canadian French is also characterized by a more informal way of speaking, which could; therefore; lead to a modification of some grammar rules that would be more respected in France.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".