Globalization, Localization, and the Preservation of French in North America (including Creole Relevancies)
Bibliographic record
Abstract
Abstract A panoramic overview of French in North America (and related creoles) reveals a complex interaction of vectors characterizing both globalization and localization. Though globalization threatens the preservation of local linguacultural integrity, it can be harnessed as a potential solution to that same threat (howbeit partial) by resorting to planned international immigration in order to swell local ranks of francophones throughout Canada, by recruiting an international francophone teaching corps to raise up the next generation of francophones in Louisiana, or by using the same democratized media facilitating globalization itself to commoditize and broadcast local legacy language and culture to an unbounded audience. However, whether in Ontario, Acadia, Louisiana, or Maine, globalization used as a method for preserving French seems to come with a price, namely the devernacularization of legacy French and its potential replacement with a different French. Conversely, though unplanned, when felicitous vectors merge, globalization becomes the agent leading to the creation of a localized French (or Haitian Creole) space where previously there had been little awareness of one, as in New York City and Florida. The most recent figures available (at the time of writing, May 2024) for the relevant populations of francophones and creolophones are provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".