Evaluating the validity of census data for tracking speaker numbers: an investigation of Canada’s Indigenous languages
Bibliographic record
Abstract
Many national censuses collect data on language use, offering valuable insights for tracking language vitality and guiding policy decisions. However, little is known about the reliability of these data. This article proposes a framework for assessing the reliability of census-based speaker counts, focusing on three key aspects: coverage, accuracy, and consistency. We apply this framework to Canadian census data from 2001, 2006, 2011, 2016, and 2021, examining nearly 60 Indigenous languages. Our analysis shows significant improvements in coverage over time, particularly for languages with small speaker populations or those previously grouped under broader ‘macrolanguages.’ However, we find that the census tends to overestimate speaker numbers for less commonly spoken languages and underestimate them for more widely spoken ones, especially between 2006 and 2016. Overall, the consistency of census data aligns with that of traditional sources. In light of our results, we recommend prioritizing more recent census data and exercising caution when using figures for languages that were previously grouped under macrolanguages.
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 | 0.040 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".