From Unsettling to UN/making: One Settler’s Critical Methodology for Disrupting Anthropocenic Perspectives and Gestures Towards Land within the Visual Arts
Bibliographic record
Abstract
Hyper-sensitive to my settler history amidst a material culture that remains complicit in the ecological destruction of Land as a multi-species being, From Unsettling to UN/making is an interdisciplinary research-creation PhD that works at the intersections of art, ecology, ethics, and aesthetics to recognize how today’s global industrial modes of production, consumption, dissemination, and discard are neo-colonial forms of ecological, and therefore cultural genocide. Particularly unsettled by how the visual arts perpetuates anthropocenic perspectives and gestures, this thesis begins by investigating how past approaches to unmaking throughout art history often aligned with acts of destruction or self-destruction. Proposing a new interdisciplinary approach to UN/making that aligns with acts of care and repair, research and creative outputs were primarily formulated through the writing of political theorist, eco-feminist, and vital materialist Jane Bennett, as well as the writing of Unangax̂ scholar Eve Tuck, Natalie Loveless, and Natasha Myers to arrive at an assemblage actions or processes that help to prevent or redress harm. Initially driven through the deconstruction and reconfiguration of existing artworks, decolonial theory, environmental research, and new materialist thinking led to questioning the conceptual foundations of Land-based art practices and Euro-colonial aesthetics carried forward through methods, mediums, modes, and iconography of Canadian traditions of fine art. Out of a desire to understand how creatives and cultural institutions might work together to bring creative practice more into relation with the timelines, liveliness, and needs of more-than-human ontographies, my final outcomes are the result of employing different methods of dealienation, decentring, degrowth, and decolonization to arrive at an UN/making Methodology. This adaptable framework for UN/making harm is designed to help usher in more eco-ethical approaches to creative production and building community outside of accelerated, elitist, racist, and sexist capitalist systems that keep the culture industry beholden to harmful ways of thinking and doing, as well as refocus attention to Canada’s Truth and Reconciliation Calls to Action as they pertain to the treatment and use of Land, education, and the production, presentation, and dissemination of art.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.085 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".