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Record W4236158464 · doi:10.32920/ryerson.14649279

Canada’s Response to the “War on Terror”: A New Era of National Security, Erosion of Rights and Racial Injustice

2021· preprint· en· W4236158464 on OpenAlexaffabout
Waheeda Rahman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsToronto Metropolitan UniversityECW Press (Canada)Global Affairs Canada
Fundersnot available
KeywordsInjusticePolitical scienceTerrorismNational securityHuman rightsImmigrationPoliticsGovernment (linguistics)DemocracyPublic administrationState (computer science)LegislationEconomic growthPolitical economyDevelopment economicsSociologyLaw

Abstract

fetched live from OpenAlex

Echoing Canada's historical treatment of immigrants, the post- 9/11 era has brought terrorism and national security issues to the forefront of the political agenda by dividing immigrants based on race, colour, religion and country of origin (Kruger, Mulder and Korenic, 2004). The research critically examines the major security legislation employed by the Canadian government since the events of September 11, 2001, in order to highlight the impact on marginalized communitites, in particular "Muslims" and "Arabs". The paper will examine through key informant interviews, the affect the new security agenda has had on targeted individuals and on the advocacy efforts of social movements and social activists. The paper takes the position that this new era of national security undertaken by the state has resulted in a two-tiered justice system, where certain groups are now being targeted by government and security agencies, while there is an erosion of democratic rights of all Canadians.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.881

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0280.008
Scholarly communication0.0070.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.316
Teacher spread0.297 · 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 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
Published2021
Admission routes2
Has abstractyes

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