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Record W4416228995 · doi:10.1002/dvr2.70041

CANTech Justice: Grassroots Responses to Digital Bias and Technological Power in the Canadian Context

2025· article· en· W4416228995 on OpenAlexafffundabout
Zeinab Farokhi, Evangeline Holtz‐Schramek

Bibliographic record

VenueDiversity & Inclusion Research · 2025
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsMcMaster UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrassrootsForegroundingIndigenousContext (archaeology)Technological changeDominance (genetics)MilitarizationEconomic Justice

Abstract

fetched live from OpenAlex

ABSTRACT Our co‐authored article investigates how grassroots arts and culture organizations engage technology as a site of justice‐oriented practice. Drawing on primary‐source interviews with Canadian grassroots arts and culture organizations, we outline an EDI‐infused technological praxis we call CANTech Justice (CTJ). CTJ provides a framework for understanding how cultural and artistic organizations resist exclusionary technologies and imagine justice‐centered alternatives. Rather than rejecting digital tools outright, these organizations engage in re‐purposing and re‐scripting—transforming existing technologies through creative, relational, and politically‐grounded practices that challenge dominant design norms and assert community control. CTJ also responds to the dominance of U.S.‐based technological infrastructures and frameworks in digital justice discourse. It does so by foregrounding locally‐rooted, intersectional approaches to technological agency. However, CTJ operates within the Canadian settler state—a nation built on land theft, resource dispossession, and cultural genocide of Indigenous peoples. These conditions set clear limits to what CTJ strategies can achieve, particularly in relation to decolonial interventions.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0020.015
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 head0.103
GPT teacher head0.386
Teacher spread0.283 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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