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The Search for Regulatory Excellence

2015· article· en· W6996315364 sur OpenAlexaboutno aff

Notice bibliographique

RevueeYLS (Yale Law School) · 2015
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueRegulation and Compliance Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésExcellenceGovernment (linguistics)Work (physics)RegulatorBest practiceFace (sociological concept)Democracy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Few government professionals today work so vitally at the front lines of human welfare as do regulators. Around the world, they strive to protect their societies’ members from the many risks associated with economic activity and to solve other important problems. Many of them face daunting societal expectations, presented with vast and often uncertain challenges that call for sound judgment and swift but judicious action. They need to find ways to engage productively with regulated industry, other governmental institutions, and various segments of the public affected by the work they do. Faced with the need often to integrate and achieve multiple objectives, and to do so in a manner consistent with democratic principles and the best available scientific knowledge, regulators face monumental challenges. Under such demanding circumstances, what does excellence mean for a regulatory institution? How can a regulator move forward and measure progress and improvement? Nearly every other field of endeavor has its standards of excellence, from the arts to medicine. What about regulators? What is their equivalent of a Nobel Prize? To determine what it means, and what it takes, for a regulator to be excellent, the Penn Program on Regulation (PPR) spent much of the past year working on a major, multi-pronged initiative, convening dialogue sessions and conducting research. Sponsored by the Alberta Energy Regulator, the regulator of energy development in the Canadian province of Alberta, PPR’s Best-in-Class Regulator Initiative has brought together leading authorities on regulation from around the world to identify attributes of regulatory excellence and methods for regulatory performance assessment. PPR has also convened two major dialogue sessions with a wide variety of interested and affected organizations and individuals from Alberta, including landowners, industry groups, municipal governments, environmental organizations, Aboriginal communities, and others. PPR released this week more than twenty reports and papers from this project. Some of these reports summarize the dialogue sessions PPR convened, while others comprehensively distill and synthesize the exhaustive academic research on what works (and what doesn’t) in government regulation. In addition, more than a dozen international experts provide their own incisive and original answers to the question of what makes a regulator excellent. In this series, The Regulatory Review is featuring essays that draw from the Best-in-Class Regulator Initiative. To launch the series, we are honored to post the prepared remarks of Dame Deirdre Hutton, the Chair of the U.K. Civil Aviation Authority, who delivered the dinner keynote address earlier this spring at PPR’s international expert dialogue held at the University of Pennsylvania Law School. We also are pleased to include in the series a summary account of the opening keynote presentation delivered at that same dialogue by Dr. David Kessler, former Commissioner of the U.S. Food and Drug Administration. In addition, the series features a synthesis of two other dialogue sessions, held with stakeholders and Aboriginal representatives in Alberta, prepared by The Regulatory Review’s immediate past Editor-in-Chief, Jessica Bassett. We also feature an essay on rating regulatory performance by Cary Coglianese, the Edward B. Shils Professor of Law and Director of the Penn Program on Regulation at the University of Pennsylvania Law School. Coglianese, who leads the Best-in-Class Regulator Initiative and advises The Regulatory Review, is now preparing a final report on the initiative that will be released later this year.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,774
Score d'incertitude au seuil0,992

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,048
Tête enseignante GPT0,271
Écart entre enseignants0,223 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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