Take the train and climb the social ladder. The role of geographical mobility in the fight against inequality in Quebec
Bibliographic record
Abstract
Despite initiatives to promote equality of opportunity, the reproduction of inequalities from generation to generation has worsened in Quebec in recent decades. Youth who grew up in a less advantaged environment are more likely to remain at the bottom of the ladder as adults. We know that education is a key factor in social mobility. A CIRANO study looks at the issue from another angle, that of geographic mobility. The authors follow the career paths of nearly 1.4 million young people and show that the lack of social mobility affects more strongly young people who grew up outside major cities, particularly those who still live there in their early thirties. This study is the first to examine the influence of geographic mobility on intergenerational income transmission in Quebec. It is based on Statistics Canada’s Intergenerational Income Database (IID), which has a longitudinal structure that tracks children to late stages of adult life. The data come from the Canada Revenue Agency’s tax data files and provide access to parent and child income information from 1978 to 2016. In terms of geographic mobility, analyses show that the deterioration of social mobility in Quebec is mainly the result of two phenomena: on the one hand, the deterioration of the socio-economic status of young people residing outside major urban centres at age 16 and having grown up in a family at the bottom of the income distribution, and improving the situation of young people from the same regions who grew up in families at the top of the income distribution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".