Bibliographic record
Abstract
Professor Karol Krotki always referred to Jacques Henripin as the "dean of Canadian demography."The title remains very legitimate.After publishing his dissertation as La Population canadienne au début du XVIII siècle (P.U.F., 1954), he produced one of the longest lasting products in the 1961 census monograph series, as Tendances et facteurs de la fécondité au Canada (1968), then with Evelyne Lapierre-Adamcyk, the results of a fertility survey in Quebec as La Fin de la revanche des berceaux: Qu'en pensent les Québécois?(P.U.M., 1974).For the green paper on immigration he did L'immigration et le déséquilibre linguistique (1974), which was later expanded as the classic La Situation démolinguistique du Canada: évolution passée et prospective, with Réjean Lachapelle (Institute for Research on Public Policy, 1980).The second fertility survey in Quebec was published as Les Enfants qu'on n'a plus au Québec (P.U.M., 1981).Four further books followed his retirement from the University of Montreal: the cunningly entitled Naître ou ne pas être (1989), Souvenirs et réflexions d'un rochon (1998), Les Enfants, la pauvreté et la richesse au Canada (2000), La Métamorphose de la population canadienne, before this volume on Pour une politique de population (2004).As with the other more recent publications, this is a "think piece", well informed by the evolving demographics.He starts by observing that any policy is necessarily a judgement accompanied by an intervention, and that a judgement
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".