Notice bibliographique
Résumé
Everyone wants fair taxation, but what do we mean by it? What impact can taxation have on inequalities? To what extent should the tax system be used to redress them? And do we (truly) want it to do so? Based on extensive new tax law data, and informed by multidisciplinary insights, this book offers answers to what are arguably some of the most challenging questions of our times.Why this book?Over the last two decades, the term “fair taxation” has become ubiquitous in public debate. This is undoubtedly linked to both the growing social concerns about income and wealth inequalities, and the increased awareness of other inequalities, such as in gender and race, and their intersections. Yet, there is also a political economy dimension to this increased popular awareness of “fairness” in tax policy; the term is sufficiently elastic to cover different taxation preferences, simple enough to be intuitively understood by voters, and suitably pro-social to convey a compelling story. From a normative perspective, however, it is precisely this conceptual elasticity that renders the term problematic.Everyone wants fair taxation, but what do we mean by it? What impact can taxation have on inequalities? To what extent should the tax system be used to redress them? And do we (truly) want it to do so?This book offers an answer to these questions. Based on extensive new tax law data – spanning the whole tax system, from tax policy to tax administration, and collated by over 60 academics located in over 30 countries – it presents a novel analytical and conceptual framework of taxation and inequalities, one that is informed not solely by tax law, but also by legal theory, human rights, constitutional and administrative law, as well as by a variety of other disciplines, including public economics, political economy, political science, moral philosophy, sociology, and moral and social psychology. The aim is both to fill a critical scholarship gap and to inform policy, contributing to what is perhaps the most challenging question faced by tax policymakers of our times: How can we build a fair tax system?
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».