English and French language ability and the employability of immigrants in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Both potential immigrants to Canada and policymakers in Canada continually compare and contrast the economic returns of immigrants' language ability and proficiency. They ask which of the two official languages has a higher economic return in terms of employment and earning. This study examines how ability and proficiency in Canada's two official languages, separately and/or jointly, influences immigrants' quick absorption into the labour market. Design/methodology/approach The study uses all three waves of the Longitudinal Survey of Immigrants to Canada (LSIC) and employs logistic regression on the relationship between employability, language ability/proficiency and various non-linguistic factors. Findings The study reports that language ability in French is as valuable as language ability in English for immigrants who are aspiring to work, full-time or part-time, when they arrive in Canada. The advantages of language ability and proficiency continue a few years after an immigrant's arrival. Using disaggregated speaking, reading and writing competencies, the authors observe that speaking proficiency in English has a greater impact on employability than reading and writing in English. Originality/value There are very few studies looking at the effects of language ability and proficiency on the employability of immigrants in countries with multiple official languages. Most studies are mainly focused on earning and not employability. This study is focused on employability, particularly in the context of Canada. Furthermore, this study specifically disaggregates the impact of speaking, reading and writing competencies in both languages on employment in Canada.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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