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Record W3041523547 · doi:10.7290/ijns060107

Consideration of Administrative Monetary Penalties in Nuclear Safety and Security

2020· article· en· W3041523547 on OpenAlexaffabout
Jelena Vucicevic, Edward R. Waller

Bibliographic record

VenueInternational Journal of Nuclear Security · 2020
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSanctionsLegislationCommissionBusinessControl (management)Computer securityRisk analysis (engineering)Law and economicsLawEconomicsFinanceComputer sciencePolitical science

Abstract

fetched live from OpenAlex

An Administrative Monetary Penalty (AMP) is a penalty imposed by the Canadian Nuclear Safety Commission (CNSC), without court involvement for a violation of a regulatory requirement. An AMP can be applied against any individual or corporation subject to the Nuclear Safety Control Act, which regulates the development, production and use of nuclear energy and the production, possession and use of nuclear and radioactive material. However, AMPs are not the same as criminal offences. They are civil sanctions which try to secure compliance through the application of monetary penalties for non-compliance with regulatory requirements. The AMP program was introduced in 2013 in Canada and to this date over 30 penalties have been issued. In all of these cases, the violations were related to handling and security of radioactive material. Based on these issued penalties investigations were conducted to discover pros and cons of the AMP system and to propose potential improvements for future implementation. This paper also addresses some of the complicated issues of the system, such as the economic aspect of the process, and the subjectivity and relative ease of issuing these penalties. In order to improve nuclear security in Canada, the regulator must be aware of possible violations of the Nuclear Safety Control Act and work on prevention of these violations. It is postulated that current AMP policy may not motivate individuals or corporations to report violations. The paper gives recommendations on modifications which could be implemented to motivate self – identification of violation, and give significant benefit to the AMP system. Other than the issued AMPs, the paper will analyze data obtained through the survey conducted on human readiness to self – identify violations in the nuclear industry under different circumstances. This confirms that the modified AMP policy would improve the body of knowledge and provide significant information on violations of the Nuclear Safety Control Act and improve nuclear security.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.420

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)0.0000.000

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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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 designQualitative
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
Published2020
Admission routes2
Has abstractyes

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