Indigenous Knowledge Sharing in Contemporary Art and Higher Education through Relational Ethics, Indigenous Ontology and Black Ecology
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.081 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".