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Record W4236350053 · doi:10.1108/oxan-db221885

Security bill will boost Canada’s cyber capabilities

2017· other· en· W4236350053 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2017
Typeother
Languageen
FieldSocial Sciences
TopicMilitary and Defense Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPledgeLegislationNational securityPolitical sciencePublic administrationLawGovernment (linguistics)EnforcementLaw enforcementBureaucracyParliamentComputer securityPoliticsComputer science

Abstract

fetched live from OpenAlex

Subject Bill C-59 and Canadian national security legislation. Significance Canada’s governing Liberals have introduced Bill C-59, omnibus national security legislation that promises to be the most significant overhaul of the national security and intelligence architecture since the creation of the Canadian Security and Intelligence Service (CSIS) in 1984. The proposed legislation follows through on a campaign promise by Prime Minister Justin Trudeau to remove unpopular elements from Bill C-51, a legacy of the Conservative government of former Prime Minister Stephen Harper that drew criticism on rights grounds for sweeping powers granted to law enforcement and intelligence to assiste counterterrorism efforts. Impacts Privacy concerns will be a persistent obstacle to Ottawa increasing cyber cooperation with the United States. The bureaucratic overhaul is likely to improve the efficacy of surveillance operations and intelligence analysis. China following through on its recent pledge not to hack Canadian commercial secrets probably depends on bilateral ties warming.

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)0.001
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.099
Threshold uncertainty score0.463

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0140.002
Scholarly communication0.0080.002
Open science0.0010.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0990.014

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 head0.014
GPT teacher head0.275
Teacher spread0.260 · 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
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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