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Record W1836919055 · doi:10.1108/13590790610707555

Disclosure and sharing of sensitive information

2006· article· en· W1836919055 on OpenAlexaboutno aff
Daniel Murphy

Bibliographic record

VenueJournal of Financial Crime · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRegulatorJurisdictionBusinessInformation sharingEnforcementScope (computer science)OriginalityLawLaw enforcementLaw and economicsEconomicsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Purpose Presentation on legal risks in business cooperation in a regulated sector in Canada. Design/methodology/approach The paper reviews Canadian case law and privacy implications for businesses cooperating with regulators and the risk of such cooperation to the regulated scheme. Findings The public interest in fostering regulator to law enforcement cooperation creates a potential risk to the regulator's scheme whenever the cooperation creates a risk that the regulator is a mere alter ego to criminal law enforcement. Practical implications Case law in Canada considers the scope of regulator compliance functions and the impact of sharing information with criminal investigators. Too much sharing, without concern for the regulated environment may risk regulator's authority to obtain business information absent a pre‐existing independent court authorization. Originality/value The paper is valuable to any regulator in a common law jurisdiction.

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)0.013
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.516
Threshold uncertainty score0.973

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0090.009
Scholarly communication0.0080.003
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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 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

Citations1
Published2006
Admission routes1
Has abstractyes

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