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Record W2266178667

The Role of ISPs in the Investigation of Cybercrime

2006· article· en· W2266178667 on OpenAlexaff
Ian R. Kerr, Daphne Gilbert

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCybercrimePersonally identifiable informationThe InternetInternet privacyIntermediaryLaw enforcementLegislatureBusinessConventionEnforcementComputer securityPolitical scienceLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

This chapter examines the new role internet service providers (ISPs) will play in the fight against cybercrime. Examining the legislative model that is being considered in various jurisdictions around the world, the authors argue that adopting this approach will lower the threshold of privacy protection. Moreover, it will drastically alter the relationship between ISPs and the individuals who have come to depend on them to properly manage their personal information and private communications. The authors begin with a brief investigation of the role of ISPs as information intermediaries. The authors then examine a recent case which held that an ISP acted as an of the when it voluntarily assisted the police in an investigation by disclosing a customer's personal information and private communications. The changing nature of the relationship between ISPs and the as manifested in the agent of the state concept, are further explored through an articulation of various kinds of investigatory information that can be collected by ISPs on behalf of the police. This is followed by a discussion of the call for a lower threshold for obtaining such information in the European Convention on Cybercrime. The authors conclude by arguing that the shifting architecture of our communications infrastructure must incorporate various safeguards that will not only further the goals of national security and law enforcement, but will also preserve and promote personal privacy.

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)0.002
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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
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

Citations9
Published2006
Admission routes1
Has abstractyes

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