Revenu de base inconditionnel : quel instrument pour quelle justice fiscale ? Réflexions à partir du cas des « gilets jaunes » en France
Bibliographic record
Abstract
Cet article se propose d’interroger la place et le rôle d’un revenu de base inconditionnel (RBI) dans le cadre d’une réforme fiscale. Partant du constat que le mouvement français des « gilets jaunes » révèle une tension croissante entre les deux principes de légitimité démocratique de l’impôt (les capacités contributives et le bénéfice), il présente une évaluation critique de la manière dont un RBI pourrait la résoudre ou l’atténuer. Les différentes conceptions de la justice fiscale qui transparaissent à travers la variété des approches du RBI sont tout d’abord mises en relief à l’aide d’une série d’alternatives : familialisation vs individualisation ; simplification vs égalisation ; allocation vs crédit d’impôt ; activation vs libération. À l’aune de cette grille de lecture, la possibilité que le RBI réponde adéquatement à une rupture d’équivalence entre contribution et bénéfice est ensuite discutée. Le RBI pourrait-il représenter la distribution d’une juste part ? Ou dérogerait-il au contraire à un principe de réciprocité nécessaire à la réalisation de la justice fiscale ?
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".