Bibliographic record
Abstract
Un bilan rigoureux et incontournable de la croissance des inégalités et un avertissement salutaire quant à la pertinence et aux limites du modèle québécois. Alain Noël, professeur de science politique, Université de Montréal En analysant l’évolution du 1% le plus riche au Québec, Nicolas Zorn montre bien comment les institutions jouent un rôle crucial dans la modération ou l’élargissement des inégalités, bien davantage que l’innovation technologique ou la mondialisation. Ce livre est important pour ceux qui se préoccupent de l’accroissement des écarts de revenus et il explique ce que nos sociétés peuvent accomplir pour s’en prémunir. Emmanuel Saez, professeur d’économie, UC Berkeley Ce livre fournit la description la plus complète et la plus claire sur le 1% québécois, et est porteur d’une leçon essentielle : l’augmentation des inégalités n’est pas inéluctable, c’est avant tout un choix politique. Gabriel Zucman, professeur adjoint d’économie, UC Berkeley
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 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".