Uncomfortable Knowledges and Transformative Learning: Reimagining the Museum in the Art of Gustafsson&Haapoja
Bibliographic record
Abstract
Destructive human action is causing interconnected ecological and social challenges on an unprecedented scale.Scholars and artists from varied fields have critically expressed their concern about this in their research and practice.In this article, we interrogate the transformative potential of critically engaged art by analysing the work of the Finnish artist duo Gustafsson&Haapoja, a collaboration between writer Laura Gustafsson and visual artist Terike Haapoja.Gustafsson&Haapoja's work focuses on the intersecting human exceptionalist, racist, imperialist, patriarchal, and capitalist histories of violence towards nonhuman animals and dehumanised humans.These histories often provoke uncomfortable affects.As such, they can be challenging to confront.To account for this difficulty, we approach Gustafsson&Haapoja's art through the idea of transformative learning, a process designed to shake established thinking and behavioural patterns.We investigate how Gustafsson&Haapoja's art-and art more generally-could function as a transformative learning resource and enable sudden ruptures in hegemonic cultural norms, privileges, and power positions.We focus on how transformative learning emerges through central features in Gustafsson&Haapoja's work: (1) their investigation and reimagination of the museum, an institution historically tied to notions of humanity and human action; and (2) their critical dissection of the complex relationship between Western-centric conceptualisations of humanity and its "others."The article is based on a theoretical discussion and a qualitative analysis of works, exhibitions, and texts published by Gustafsson&Haapoja's Museum of Becoming (2020-21) as well as an interview with the artists.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.070 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".