Línguas de imigrantes em " países novos": o caso do Canadá visto através de dados censitários
Bibliographic record
Abstract
O artigo enfoca as linguas outras que nao o ingles e o frances, faladas por imigrantes e seus descendentes, no Canada, tomado como exemplo de “pais novo” em que a imigracao foi muito significativa. Procurou-se avaliar, sobretudo, o grau de transmissao e persistencia das linguas que por sua expressao estatistica possibilitassem o exame individualizado. As abundantes informacoes propiciadas pelo recenseamento do Canada de 2001 forneceram o arcabouco essencial da analise, efetuada com visao regionalizada, na qual foi destacada a parte do Canada de lingua inglesa, incluindo abordagens mais localizadas em meio a esse espaco, e a de lingua francesa, enfocando especificamente Montreal Palavras-chave: Canada. Linguas. Imigrantes. Assimilacao linguistica. Immigrant languages in “new countries”: the case of Canada examined through census data The article concerns languages others than English and French, spoken by immigrants and their descendants in Canada, taken as example of a “new country” in which immigration was very significant. The main intention was to evaluate the degree of transmission and persistence of the languages whose statistical expression allowed an individual examination. The plentiful data supplied by the Canadian Census of 2001 delivered the essential framework of the analysis, carried out in regionalized approach, with a focus on the Anglophone part of Canada, including more local appreciations inside this space, and the Francophone part, selecting specifically Montreal. Key words: Canada. Languages. Immigrants. Linguistical assimilation.
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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 | 0.004 | 0.013 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 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".