Resisting, Recovering, Reflecting, and Reimagining Design Education
Bibliographic record
Abstract
New, ongoing, and unremitting urgencies are aggregating at a furious rate – climate, inequality, populism, poverty, artificial intelligence, and unchecked capitalism. This uncertainty is daunting, but as bell hooks reminds us, “[t]he classroom, with all its limitations, remains a location of possibility” (1994: 207). The DRS EdSIG invites contributions that explore design education and its limitations and possibilities in the following ways: Resisting Education / The Education of Resistance What forms of design education should be resisted? How has design education been complicit in supporting existing orders? / What does it mean to educate future designers and design researchers for resistance, and what kinds of resistance should we be educating for? Recovering Education / The Education of Recovery What should we recover design education from? And what might we need to lose? / What does educating future designers and design researchers for recovery mean? Reflecting on Education / The Education of Reflection Which established epistemologies of design education should we critically reflect on? What modes of reflection are needed nowadays? / How can the education of future designers and design researchers utilise reflection to challenge existing orders and worldviews? Reimagining Education / The Education of Re-imagination How do we reimagine? With whom and what are the fruits of this re-imagining? / What does educating future designers and design researchers for re-imagination mean? We invite speculations, positions, possibilities, case studies, and empirical investigations that reconsider our knowledge, methods, and approaches in design education at undergraduate, postgraduate, and doctoral level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.091 |
| Scholarly communication | 0.025 | 0.036 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".