Canadian Language Ideology as Reflected in Censuses: a Better Alternative to American Censuses
Bibliographic record
Abstract
The article analyzes how differences in Canadian and American linguistic ideologies affect the tasks of population censuses, the wording of language questions, and the information extracted from the census sheets. The Canadian ideology of multiculturalism and the preservation of linguistic diversity, unlike the American ideology of the “melting pot”, is characterized by great attention to the use of official languages, indigenous languages and immigrant languages, which is reflected in the inclusion of detailed questions about the inventory of languages and respondents' language competencies. Processing data from the census allows Canadian socio-linguists to form a significant number of language indicators describing various aspects of Canadian institutional bilingualism and multilingualism, monitor compliance with the rights of speakers of different categories of languages, and predict changes in the language situation in the country. The relative decrease in the share of speakers of official languages (English and French) is not considered by the Canadian authorities as a threat, while the United States are very wary of an increase in the proportion of hispanophones in the structure of the American population. From the point of view of content and full use of information, Canadian population censuses can be considered role models in other countries.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.029 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".