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Record W3122100396 · doi:10.1007/978-3-030-29980-4_7

Public Servants and Regulator Capture in Energy and Environmental Governance

2021· book-chapter· en· W3122100396 on OpenAlexaff
Cameron Holley, Amanda Kennedy, Tariro Mutongwizo, Clifford Shearing

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLegitimacyRegulatory stateCorporate governanceAccountabilityPublic administrationPolitical scienceGovernment (linguistics)Context (archaeology)CONTESTState (computer science)Public interestPublic relationsBusinessLawPoliticsGeography

Abstract

fetched live from OpenAlex

The rapid pace of change, uncertainty, and social contest associated with emerging regulatory spaces create challenges for public servants. In particular, their capacity to act in the public interest may be constrained as contesting interests vie for influence in nascent regulatory environs. This chapter explores these issues and the potential for regulatory capture in the context of unconventional gas and its regulation. Empirically based case studies of regulators in Texas and Colorado in the USA and Queensland, Australia, are relied on. The existing regulatory frameworks governing unconventional gas in each state are considered to enable a thorough examination of the landscape of which public servants and their regulatory agencies are a part. The chapter demonstrates that the speed at which unconventional gas exploration is taking place creates challenges for public servants and regulatory agencies, as laws may not be aligned to practice. The chapter draws on its findings to reflect on the specific regulatory practices that are needed to ensure the accountability and legitimacy of the public sector in such contested spaces, including reforming state regulatory systems and pursuing alternative governance pathways in which relationships between industry, government, and society might be reconfigured.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.955
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.173
Teacher spread0.150 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractno

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