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Indigenous Knowledge Sharing in Contemporary Art and Higher Education through Relational Ethics, Indigenous Ontology and Black Ecology

2025· article· en· W4417215184 on OpenAlexfundno aff
Markus Hallensleben

Bibliographic record

VenueInternational Journal of Critical Diversity Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaConcordia University
KeywordsIndigenousTraditional knowledgeOntologyColonialismHigher education

Abstract

fetched live from OpenAlex

I will link two artistic examples from the Berlin Biennale 2022 that engage with Europe’s past, present and future entanglement with colonialism, to Jo-ann Archibald Q’um Q’um Xiiem’s Indigenous Storywork as one instance of a “new relational ethics” and an Indigenous, “non-colonial” way of sharing knowledge. In particular, I will discuss Moses März’s engagement with Édouard Glissant’s Philosophy of Relation in his “Maps for a Creolisation of the Word”. The impact of an ongoing colonialism is also critically assessed by Uriel Orlow, in his “Reading Wood (Backwards)”, who asks: “What happens when forest becomes library?” Both artists question western colonial concepts of extracting nature and resources, collecting and archiving material, as well as producing books, maps and texts as methods of unstainable knowledge transfer, whether within or outside academia. With the aim for working towards a relational ethics, whether through an “ecology of knowledges”, a “Black ecology” or Indigenous ontology, both artworks point to an Indigenous knowledge centred future and way of rebuilding the museum and university as open places, rather than as closed-up institutions, for sharing knowledge in a respectfully collective, reciprocally collaborative, holistically interrelated, synergetic interconnected, accountably reverent and responsibly relevant manner.

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 categoriesnone
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 score0.294
Threshold uncertainty score0.438

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.189
GPT teacher head0.460
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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 routes1
Has abstractyes

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