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Record W3209054816

Colder Now: Surveillance as Contemporary Colonialism in Canada

2020· dissertation· en· W3209054816 on OpenAlexaboutno aff
Stefy McKnight

Bibliographic record

VenueQSpace (Queen's University Library) · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicAsian American and Pacific Histories
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismPolitical scienceGeographyHistoryData scienceLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

Canada is a settler state built on systemic racism, and is maintained by systems of contemporary colonialism. Systems of contemporary colonization such as internal colonialism (Tuck and Yang, 2012) take the form of the surveillance and policing of Black, Indigenous, and People of Colour. Through a white settler artist-scholar lens, this research exposes instances of surveillance and policing in Canada, by analyzing policies such as the “Anti-terrorism Act, 2015” (Bill C-51) and its effect on non-white citizens. This portfolio of essays and visual work draws knowledges from my experience as a white settler artist-scholar. I use research-creation as a methodology for situating myself in this narrative, while complicating my identity through self-reflexivity. I critically engage with and attempt to disrupt the systems that continue to privilege me because of my white settler identity. More importantly, I see my creative practice as a method of decolonizing surveillance studies, while also rethinking and unsettling surveillance as not only a practice or system, but a political identity tied to whiteness and white settlerhood. Surveillance and policing is more than a way of governing and controlling citizens, but of creating citizens. Through an analysis of function creep, public policy, and law, I theorize surveillance as a political identity and a producer of knowledge in its own right, through visual and performative means.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 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
Published2020
Admission routes1
Has abstractyes

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