The Challenges of Art Education in Designer Capitalism: Collaborative Practices in the (New Media) Arts
Bibliographic record
Abstract
Abstract This article explores the challenges to art education in the twenty‐first century as art curricula around the world begin to change so as to meet the new emergent technological realities. It is argued that within a ‘control’ society like ours, where the economic system of capitalism dictates the direction of education along with its accompanying neoliberalist philosophy of the self, art educators are faced deciding how to cope and incorporate the new media technologies into their art programmes. I try to argue that this direction should recognise the ‘affective turn’ within media and grasp the different orientations when it comes to collaboration. In the final part of the article I provide a number of artistic exemplars that illustrate the direction art education should follow, given the dire state of the world in an era that will be called the Anthropocene.
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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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.056 |
| Scholarly communication | 0.032 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".