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Non-Colonial Creative Interventions: Collective Belonging through Shared Labour, Collaboration and Active Participation

2025· article· en· W4417215183 on OpenAlexafffund
Kristina Parzen

Bibliographic record

VenueInternational Journal of Critical Diversity Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of CanadaConcordia University
KeywordsIndigenousStorytellingEthnographyPoliticsTraditional knowledgeValue (mathematics)Argument (complex analysis)Agency (philosophy)

Abstract

fetched live from OpenAlex

What does it mean to belong on traditional and ancestral Indigenous lands, unceded and treaty territories? Can Indigenous and Settler peoples achieve collective belonging? This paper grapples with these questions through critical engagement with the politics of belonging and three creative projects that offer concrete practices for doing decolonial and non-colonial work. Such methods include an ethics of care and storytelling as a way of sharing knowledge differently, which are put into practice when Settler peoples responsibly, respectfully, reciprocally and relevantly do the labour of decolonisation, collaborate with Indigenous communities and become active participants in decolonial and non-colonial interventions. The artistic projects discussed include: Leah Decter and Jaimie Isaac’s (official denial) trade value in progress (2009–2014), David Garneau and Clement Yeh’s Apology Dice (2013) and Victoria May and Marjolaine Arpin’s Kitchen Table Talk (2023). These projects are all examples of collaborations between Indigenous and Settler artists and ultimately support the argument that collective belonging is achievable only when continual processes of Indigenous centring and Settler unsettling are enacted in society.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.445
Teacher spread0.394 · 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
Published2025
Admission routes2
Has abstractyes

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