Bibliographic record
Abstract
Canadian Second World War veterans benefited from an extensive educational program similar to the U.S. G.I. Bill. Because of differences in military enlistment rates, however, a much lower fraction of Quebec men were eligible for these benefits than men from other provinces. Building on this fact, we analyse inter‐cohort patterns of education and earnings for English‐speaking men from Ontario, using French‐speaking men from Quebec as a control group. We find that the instrumental variables estimates of the return to schooling are typically as big or bigger than the corresponding OLS estimates. JEL Classification: J24, I21 Education, revenus, et le ‘G.I. Bill canadien.’ Les anciens combattants canadiens de la deuxième guerre mondiale ont bénéficié d'un programme d'aide aux études similaire au G.I. Bill américain. Relativement peu de Québécois ont cependant pu bénéficier de ce programme en raison de la faible proportion de ces derniers qui ont fait leur service militaire durant cette période. Sur la base de cette observation, nous analysons les différences intergénérationelles dans la scolarisation et les revenus des Ontariens anglophones en se servant des Québécois francophones comme groupe témoin. Nos résultats indiquent que les estimés du taux de rendements de l'éducation obtenus à l'aide de la méthode des variables instrumentales sont comparables sinon plus élévés que ceux obtenus par moindres carrés ordinaires.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".