MétaCan
Menu
Back to cohort
Record W3136163756

Limits to Secrecy: What are the Communications Security Establishment's (CSE) Capabilities for Intercepting Canadians’ Internet Communications?

2018· article· en· W3136163756 on OpenAlexaffabout
Andrew Clement

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThe InternetSecrecySuspectKey (lock)TelecommunicationsPolitical scienceInternet privacyComputer securityBusinessComputer scienceLawWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

This chapter contributes to the growing debate in Canada over mass state surveillance by shedding light on key aspects of Canada’s Communications Security Establishment (CSE) domestic internet surveillance capabilities and activities. Drawing mainly on the Snowden documents, it argues that there are good reasons to suspect that the CSE is routinely intercepting the internet communications of millions of Canadians. It relies on an exploratory analysis of Canadian internet traceroute data to estimate where and with which carriers the CSE is most likely to capture internet traffic. This analysis shows that by accessing the main switching centres of a handful of leading telecom providers (e.g. Bell, Rogers, Shaw, Telus) the CSE could surveil a large fraction of Canadians’ internet communications by establishing interception facilities within a few key cities (notably Toronto, Montreal and Vancouver). This chapter draws attention as well to CSE’s excessive secrecy about its domestic internet surveillance capabilities. It concludes by arguing that for the CSE to meet its obligations to respect international human rights and democratic norms, it must reform its practices of excessive secrecy and become significantly more transparent and accountable to Canadians, especially around its capabilities for mass interception of internet communications.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.325
Teacher spread0.290 · 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.

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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicCybersecurity and Cyber Warfare StudiesFrench-language works237,207