Offre de travail au noir en présence de la fiscalité et des contrôles fiscaux
Bibliographic record
Abstract
Cet article présente un modèle économétrique d’offre de travail sur les marchés officiel et souterrain en présence de la fiscalité et des politiques de lutte contre la fraude. Nous spécifions une fonction d’utilité quadratique afin d’imposer un minimum de structure aux préférences individuelles. Les estimations des paramètres sont obtenues par la méthode du maximum de vraisemblance à information complète. Notre approche tient compte de l’endogénéité de la probabilité de détection et de l’amende. Elle élimine ainsi le biais potentiel de dissonance cognitive associé aux variables subjectives. Nous procédons aux estimations sur données individuelles canadiennes. Nos résultats confirment l’influence des paramètres fiscaux et rejettent les hypothèses de séparabilité additive de la fonction d’utilité et de substitution parfaite des heures de travail.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".