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Record W6996315364

The Search for Regulatory Excellence

2015· article· en· W6996315364 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsExcellenceGovernment (linguistics)Work (physics)RegulatorBest practiceFace (sociological concept)Democracy
DOInot available

Abstract

fetched live from 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.048
GPT teacher head0.271
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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