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What Makes a “Best-in-Class” Regulator?

2015· article· en· W7044114064 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-clésRegulatorEnforcementProcess (computing)Session (web analytics)Regulatory focus theorySet (abstract data type)Regulatory stateFocus (optics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

How would you define an excellent regulatory authority? Would you focus on the regulator’s outputs, such as how many regulations it completes or enforcement actions it takes? Or would you focus instead on its outcomes, such as how effective those regulations and enforcement actions prove to be? Or would you focus on inputs and the process by which the regulator makes decisions and creates regulations? All of these approaches have merit. But based on two dialogue sessions organized this spring in Alberta, Canada by the Penn Program on Regulation (PPR), it appears that many stakeholders of the Alberta Energy Regulator (AER) favor an input-based answer to the question of regulatory excellence. These stakeholders repeatedly emphasized that how the regulator interacts with the public during the process of making regulatory decisions is the defining feature of a “best-in-class” regulator. PPR’s dialogue sessions were part of a larger project sponsored by the AER to identify what makes an excellent regulator and how the AER can apply and measure those characteristics. One session brought together representatives from Aboriginal communities across Alberta, while another larger dialogue included landowners, municipal officials, industry representatives, environmentalists, and Aboriginal community members from throughout the province. Although responses varied and different categories of “best-in-class” characteristics emerged, across the board stakeholders consistently stressed that the way in which the regulator engages with the public is a crucial component of regulatory excellence. For example, during the second dialogue, PPR researchers found that stakeholders’ ideas about a “best-in-class” regulator seemed to “coalesce around a set of six core” areas: “(i) institutional characteristics; (ii) regulatory decisions; (iii) transparency; (iv) public engagement; (v) enforcement; and (vi) evaluation.” Although transparency and public engagement were broken out into their own categories in a summary report PPR team members prepared following the dialogue, they actually overlap. More significantly, both transparency and public engagement factored into stakeholders’ comments in each of the other six core areas of excellence. For example, the report’s section on institutional characteristics reveals how stakeholders identified that an excellent regulator’s institutional culture should exhibit “a clear set of values, adopted following a public input process,” and that it should also reflect “a pattern of earnest collaboration with members of the public.” Stakeholders also emphasized transparency, keeping lines of communication open with the public, and including a diverse and balanced set of representatives in regulatory processes. Stakeholders at both dialogues noted that, when making decisions, a regulator should engage members of the public early on in the process and then again on a continuing basis as decisions are made. The regulator should ensure that the public’s comments are included in the ultimate decision so that the public feels it has been heard. Equally important, a “best-in-class” regulator will also explain why it has made certain decisions, giving reasons and responding in more than formulaic fashion to concerns raised during the process. According to stakeholders at the dialogues, all of a regulator’s procedures and regulations should be “accessible, readable, and understandable by all those impacted.” The regulator should also have offices physically located near different parts of community, with staff working in the field not only to inspect regulated facilities but also to engage with the public. Stakeholders said they wanted to be kept apprised of ongoing decision-making and regulatory developments, even if the news might be bad for their interests. An excellent regulator, they said, will also keep the public informed of enforcement actions and involve the public in setting goals and evaluating how well those goals have been met. Stakeholders at both dialogues mentioned the importance of a regulator treating members of the public fairly and with respect. These considerations seemed particularly important to representatives from various Aboriginal communities. These communities’ representatives believed that an excellent regulator would ensure that all stakeholders have the resources to participate in the regulatory process. They stressed that an excellent regulator will be sympathetic to and understanding of Aboriginal customs, cultures, and methods of communication. Some dialogue participants suggested that traditional Aboriginal knowledge about nature could bring important insights to a regulator’s assessment of changing environmental conditions. According to stakeholders, an excellent regulator seeks to build trust with all segments of the public. Aboriginal representatives made a particular point to emphasize the importance of relationship-building. They thought that an excellent regulator would include Aboriginal perspectives in all of its decisions and ensure these communities’ concerns are heard. Some dialogue participants stated that an excellent regulator should have at least one designated Aboriginal representative on its board, in addition to an adequate number of culturally sensitive staff members who are dedicated to reaching out to Aboriginal communities. All different types of stakeholders from throughout Alberta seemed to agree that an excellent regulator will actively seek out public input when making decisions. An excellent regulator will realize, for example, that email is not always a sufficient means of communication for many members of Aboriginal communities and for residents in more rural or remote locations. Ultimately the process of making regulatory decisions is what makes a regulator “best-in-class.”

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,824
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,003
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,003

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 Opus0,038
Tête enseignante GPT0,258
Écart entre enseignants0,220 · 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.

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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