Can we put a Number on it? Design Education Under Capitalism, Science, and Technology
Bibliographic record
Abstract
In contemporary media contexts, graphic design work no longer focuses solely on the aesthetic or representational functionality of 2-dimensional design artefacts. In response to public expectations, economic imperatives, and dominant STEM narratives embracing technical innovation, design education is gradually reshaping and retooling itself in line with techno-capitalist purposes. Design education is becoming more science-driven, introducing more data-driven research methodologies and positivist approaches such as behaviourism, computer science, design engineering, and interactive design. As the discipline of design continues to evolve, there is a growing debate over whether design-making should fully embrace technology and digital media, displacing traditional design work and curriculum with data science epistemologies that align design education with corporate interests, big data, algorithmic culture, and ‘surveillance capitalism’ (Couldry & Mejias, 2019; Crawford 2021; Zuboff, 2019). This essay provides a critical perspective on current trends in design education sectors that embrace tech-oriented methodologies and design practices. I argue that uncritically translating the discipline of design for digital culture risks exacerbating these equity issues and power relations. Furthermore, this essay discusses what priorities in design education foster critical and even activist forms of creativity alongside building essential design skills as well as technical and aesthetic competences.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".