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Record W2014154185 · doi:10.2202/1538-0653.1494

Public Complaints and Alberta's Environmental Regulation

2006· article· en· W2014154185 on OpenAlexaffabout
Heather Eckert

Bibliographic record

VenueTopics in Economic Analysis & Policy · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnforcementLegislationGovernment (linguistics)BusinessEnvironmental regulationPublic consultationAction (physics)Public disclosurePublic economicsPolitical scienceEconomicsEngineeringLaw

Abstract

fetched live from OpenAlex

Abstract In recent years, different levels of government in Canada and the United States have claimed that public complaints are an important source of information for the enforcement of environmental regulations. Public complaints provide monitoring at a lower cost than inspections but are inaccurate because citizens lack the information to assess a potential environmental threat properly. Little existing literature examines the use of public complaints in enforcing environmental regulations. Using a dataset of environmental reports in the province of Alberta between January 1996 and September 2002, this paper details the use of public complaints to enforce Alberta's environmental legislation and examines the effectiveness of public complaints in different industries and for different environmental threats. I find that that the majority of public complaints are simply odour complaints, and that public complaints are investigated less often over time and very rarely lead to enforcement action.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.219
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2006
Admission routes2
Has abstractyes

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