Le Québec économique 7 : Éducation et capital humain
Bibliographic record
Abstract
Nearly a quarter of the Government of Quebec's expenditures are allocated to the education sector. For a society, education acts both as an engine of economic growth and as a powerful tool in the fight against poverty. Investment in education allows the accumulation of human capital, a key factor in individual development. This seventh edition of Québec économique takes stock of contemporary issues in the Québec education system. To position the analysis correctly, portraits of the primary and secondary network and then of higher education are first presented. A series of economic issues are then studied in detail. In particular, the following topics are addressed by researchers recognized in their field: the financing of higher education, the performance of the education network as well as the private and social returns to education. More information Près du quart des dépenses du gouvernement du Québec sont allouées au secteur de l’éducation. Pour une société, l’éducation agit tant comme un moteur de croissance économique que comme un puissant outil de lutte contre la pauvreté. L’investissement en éducation permet l’accumulation du capital humain, facteur déterminant du développement individuel. Cette septième édition du Québec économique fait le point sur les enjeux contemporains du système d’éducation québécois. Pour bien positionner l’analyse, des portraits du réseau primaire et secondaire puis de l’enseignement supérieur sont d’abord présentés. Une série d’enjeux économiques sont ensuite étudiés de manière détaillée. Sont notamment abordés par des chercheurs reconnus dans leur domaine : le financement de l’enseignement supérieur, la performance du réseau de l’éducation ainsi que les rendements privés et sociaux de l’éducation. Plus d’information
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 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".