English as the Lingua franca and the Economic Value of Other Languages: The Case of the Language of Work in the Montreal Labor Market
Why this work is in the frame
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Bibliographic record
Abstract
An important feature of Canada is that it has two official languages, English and French, and that one of them, English, is also the international <italic>lingua franca</italic>. This situation may have particular policy implications. Within Canada, the Montreal metropolitan area presents an interesting case in point: it has a majority of native French speakers, an important minority of native English speakers, and many immigrants from various linguistic backgrounds who try to make their way into the labor market. Using confidential micro-data from the 2006 Canadian Census, this chapter investigates the determinants and the economic values of the use of different languages at work in Montreal. Workers are divided into three groups: <italic>French</italic>, <italic>English</italic> and <italic>Other</italic> mother tongues, and indices are defined for the use of <italic>French</italic>, <italic>English</italic>, and <italic>Other languages</italic> at work. It is found that the use of English at work by non-English native speakers is positively related to the education level of the workers, while there is no such relationship for the use of French by native English speakers. The returns to using at work a language that is different from one’s mother tongue are analyzed with ordinary least squares and instrumental variables regressions. For the <italic>English</italic> mother tongue group, using French at work has little or no reward, while using English at work pays a lot for the <italic>French</italic> mother tongue group. For the <italic>Other</italic> mother tongues group, there is a high payoff to using an official language at work, especially English. This situation is not due to the inferior economic status of the native French speakers; it is due to the fact that English is the international <italic>lingua franca</italic>. The policy implications of the above results are discussed.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it