politiques canadiennes d’intérêt public Policies to Stem the Brain Drain – Without Americanizing Canada
Bibliographic record
Abstract
Ceux qui réclament des politiques canadiennes en réaction à la menace d’un drainage des cerveaux au profit des Etats-Unis ne tiennent pas souvent compte des facteurs qui ont, jusqu’à présent, retardé cet exode. Cette étude offre une vue holistique de la décision d’émigrer prise individuellement. La plupart des gens sont concernés par les services publics qu’ils reçoivent, ainsi que par les impôts qu’ils paient, et beaucoup aussi se soucient du civisme de la société dans laquelle ils vivent, aussi bien que des marchandises qu’ils peuvent acheter à titre personnel. Cette perspective influence l’évaluation de politiques aussi diverses que la taxa-tion, la sécurité des revenus, la santé publique et l’éducation, les incitations régionales et les investisse-ments sociaux. Une conclusion s’impose: c’est que les politiques canadiennes devraient être guidées par des objectifs de politique intérieure d’équité, d’efficacité et de croissance plutôt que par un désir d’endiguer l’émigration ou d’imiter les politiques américaines. Afin d’encourager une croissance économique cons-tante, pour le bénéfice de tous les Canadiens, les politiques canadiennes devraient prendre garde de ne pas mettre en péril les attributs sociaux, civiques et culturels qui distinguent le Canada. Calls for Canadian policies to respond to the threat of brain drain to the United States often ignore the factors that have retarded such outflows to date. This study offers a holistic view of individual decisions to migrate. Most people care about the public services they receive as well as the taxes they pay, and many also
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".