La rémunération des attributs linguistiques au Québec : résultats pour 2015 et évolution depuis 1970
Bibliographic record
Abstract
This paper presents the labour market remuneration of the linguistic attributes of men and women in Quebec for 2015 and its evolution since 1970. It uses results produced with microdata made available to researchers by Statistics Canada and drawn from nine Canadian Censuses. The analytical framework adopted is that of the theory of human capital. The differences in average labor income are presented and analyzed by calculating the net effects of linguistic attributes on labour income. These correspond to the remuneration of linguistic attributes per se and are obtained by multivariate analysis (OLS). The main results for 2015 are as follows: Individuals with the highest average labour income (Figure 1) are bilingual (allophones (Allo B), English speakers (Anglo B) or French speakers (Franco B) knowing English and French). Then come unilingual Anglophones (Anglo U) and Francophones (Fran U), then non-bilingual allophones. In 2015, the only group whose linguistic attributes were better paid (net effect, base specification)) than those of unilingual Francophones were bilingual Francophones. Let us now examine the evolution over time of the net effects of language skills for three groups –Bilingual francophones (FB), unilingual anglophones (AU) and bilingual anglophones (AB). The main finding is the clear break between 1970 and 1980-2015 in the premium earned by Anglophone, unilingual or bilingual, men. Such a premium was not present for women in 1970. The general conclusion that can be drawn from this is that our results on the net effects of linguistic attributes for 2015 are similar to those observed since the beginning of the 21st century. Bilingualism is better paid than unilingualism among Francophones and Allophones, and Anglophones are doing as well as unilingual Francophones. Ce texte présente la rémunération sur le marché du travail des attributs linguistiques des hommes et des femmes du Québec pour 2015 et son évolution depuis 1970. On y utilise des résultats produits avec des microdonnées mise à disposition des chercheurs par Statistique Canada et tirées de neuf Recensement du Canada. Le cadre analytique retenu est celui de la théorie du capital humain. On présente les écarts de revenu de travail moyen et on les analyse en calculant les effets nets des attributs linguistiques sur le revenu de travail. Ceux-ci correspondent à la rémunération des attributs linguistiques en soi et sont obtenus par l’analyse multivariée (MCO). Les principaux résultats pour 2015 sont les suivants : Les individus ayant les revenus moyens de travail (Figure 1) les plus élevés sont les bilingues (allophones (Allo B), anglophones (Anglo B) ou francophones (Franco B) connaissant anglais et français). Suivent les unilingues anglophones (Anglo U) et francophones (Fran U) puis les allophones non bilingues. En 2015, le seul groupe dont les attributs linguistiques sont mieux rémunérés (effet net, spécification de base)) que ceux des francophones unilingues sont les francophones bilingues (figure 2). Examinons maintenant l’évolution à travers le temps des effets nets des attributs linguistiques pour trois groupes-francophones bilingues (FB) anglophones unilingues (AU) ou bilingues (AB) .La principale constatation est la nette rupture entre 1970 et 1980-2015 dans la sur rémunération des hommes anglophones, unilingues ou bilingues (Figure 3). Une telle sur-rémunération n’était pas présente pour les femmes en 1970 (Figure 4. La conclusion générale que l’on peut tirer de ceci est que nos résultats sur les effets nets des attributs linguistiques pour 2015 sont similaires à ceux observés depuis le début du XXIème siècle. Le bilinguisme est mieux rémunéré que l’unilinguisme chez les francophones et les allophones et les anglophones se tirent aussi bien d’affaire que les unilingues francophones.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".