The Statistics of Bilingualism
Bibliographic record
Abstract
National censuses are rarely interested in those who know and use two or more languages, and they seldom make available statistics that reflect the bi- or multilingualism of their population. The author recounts how, over the years, he researched how many bilinguals there are in various countries. He contacted national statistical offices and census bureaus, studied their data, and perused national and transnational reports. He also interacted with official statisticians, who answered his questions and sent him unpublished data. And sometimes he went on specific quests to hunt down particular numbers or percentages that were being passed around. Here he concentrates on the results he obtained for the United States, Canada, Switzerland, and France. He ends with the holy grail many have been searching for – the proportion of bilinguals in the world. He gave an estimate back in 1982 – about half of the world’s population – and discusses how, with time, even 65 percent was proposed by some. Understanding why that was so was an adventure in its own right.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".