Bibliographic record
Abstract
This thesis explores several issues in the adaptation process of immigrants and their children in Canada. Chapter 2 investigates why second-generation immigrants are better educated than the remaining population. Using a standard human capital framework where individuals choose how much to invest in both their children's and their own human capital, I show that a gap in education can arise in the absence of differences in unobservable characteristics between immigrants and the native born. Rather, it can arise due to institutional factors such as imperfect transferability of foreign human capital and credit constraints. The model's key implication is a negative relationship between parental human capital investments and children's educational attainment, particularly in families with uneducated parents. I find strong empirical evidence of such tradeoffs in human capital investments occurring within immigrant families. Chapter 3 re-assesses the effect of living in an ethnic enclave on labour market outcomes of immigrants. I find evidence of cohort effects in the relationship between mean earnings and the proportion of co-ethnics in the CMA which vary by education level. Next, using information on the proportion of one's friends who share one's ethnicity, I test a common assumption that the enclave effect is a network effect. I find that traditional, geography-based measures of the ethnic enclave effect capture the impact of factor(s) other than social networks. In fact, the two effects generally offset each other to some degree in determining immigrant employment outcomes. Neither measure has a statistically significant effect on average immigrant earnings, at least in cross-sectional data. Chapter 4, co-authored with David Green and Craig Riddell, tests two alternative theories about why immigrants earn less than native-born workers with similar educational attainment and experience - discrimination versus lower skills (measured by literacy test scores). We find that immigrant workers educated abroad have lower cognitive skill levels (assessed in English or French) than similar native-born workers. This skills gap can explain much of the earnings gap. At the same time, foreign-educated immigrants receive no lower returns to skills than the native born. These results offer strong evidence against the discrimination hypothesis.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".