INTERGENERATIONAL PROGRESS IN EDUCATIONAL ATTAINMENT WHEN INSTITUTIONAL CHANGE REALLY MATTERS: A CASE STUDY OF FRANCO-AMERICANS VS. FRNECH-SPEAKING QUEBECKERS
Bibliographic record
Abstract
Using U.S. and Canadian census data I exploit the massive out migration of approximately 1 million French-Canadians who moved mainly to New England between 1865 and 1930 to look at how the educationalattainment and enrollment patterns of their descendants compare with those of same aged French-speaking Quebeckers. Data from the 1971 (1970) Canadian (U.S.) censuses reveal that New England born residents who had French as their mother tongue enjoyed a considerable advantage in terms of educational attainment. I attribute this large discrepancy to their exposure to the U.S. public school system which had no equivalent in Quebec until the late sixties. This result is even more remarkable given the alleged negative selection out of Quebec and the fact that Franco-Americans were fairly successful in replicating the same educational institutions as the ones existing in Quebec. Turning to the 2001 (2000) Canadian (U.S.) censuses, I find strong signs that the gap has subsided for the younger aged individuals. In fact, contrary to 30 years earlier, young Quebeckers in 2001 had roughly the same number of years of schooling and were at least as likely to have some post-secondary education. However, they still trail when it comes to having at least a B.A. degree. This partial reversal reflects the impact of the "reverse treatment" by which Quebec made profound changes to its educational institutions, particularly in the post-secondary system, in the mid-to-late 60's. Given the speed at which this partial catch-up occurred, it would appear that the magnitude of the intergenerational externalities that can be associated with education is at best fairly modest.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".