Bibliographic record
Abstract
The mobility of immigrants’ earnings and their experience in getting ahead in the Canadian labour market are reflection of the general state of economic opportunity in Canada. High or increasing degrees of upward mobility of earnings may indicate increasing opportunities for economic advancement, whereas low degrees of upward mobility or high degrees of downward mobility may reflect limited or deteriorating opportunities for economic advancement. A study entitled “Earnings Mobility of Canadian Immigrants: A Transition Matrix Approach†(CLSRN Working Paper no. 127) by CLSRN affiliates Michael Abbott and Charles Beach (both of Queen’s University) examines earnings mobility patterns of immigrants arriving in Canada over ten years after landing in Canada for three landing cohorts – 1982, 1988 and 1994 – under four separate admission classes: independent economic, other economic, family class, and refugees, in order to determine whether, and how, immigrants in one admission class fare relative to those arriving in other classes. A paper entitled “The Fertility of Recent Immigrants to Canada†(CLSRN Working Paper no. 121) by CLSRN affiliates Alicia Adsera (Princeton University) and Ana Ferrer (University of Waterloo) focuses on the fertility outcomes of migrants around the years immediately before and after migration. Using data from the confidential files of the Canadian Census for the years 1991 through 2006, the researchers examine native born-immigrant differentials in new births and find evidence of a relatively rapid growth in births during immigrant’s initial yeas in Canada. They estimate that the probability a married immigrant woman has an infant upon arrival to the country is almost half that of a Canadian-born woman with similar characteristics (4% versus 8%). The prevalence of infants in immigrant households, however, increases thereafter, coming close to that of Canadian born around two years after women migrate. There are some differences in fertility across origins that suggest that cultural differences matters. European, American and Asian immigrants show the lowest levels of fertility during the first years after migration. In fact, these groups do not reach parity with native-born women during the first five years after arrival. African and Middle Eastern immigrants, on the other hand, show the highest levels of fertility among all migrant groups, relative to the native born, earlier in the migration process.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.242 | 0.123 |
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".