Building “Working with, not for” into Design Studio Curriculum
Bibliographic record
Abstract
Design ManifesT.O. 2020 is a Participatory Action Research project currently underway in Toronto, Canada and is working with communities to uncover stories of grassroots placemaking and community building done through creative practice. An unexpected discovery during data collection highlighted how communities are still being left out of decision-making processes that directly affect their collective values and living conditions and are being disrespected by designers and researchers — exposing very large gaps in the education of designers in terms of values-based learning, design ethics, and informed methods for working with communities. This paper interrogates design pedagogy and practice in order to stimulate further discourse and investigation into how to successfully integrate ethical and responsible protocols into design curriculum to support co-design practices where social justice and equity becomes normalized in practice. In other words: giving students the tools to “work with, not for” communities. Demonstrating social conscience is ethically desirable in design education but if students are not given the tools required to work with communities through respectful and collaborative processes then we are training the next generation of designers to continue a form of hegemony in design practice that is undesirable.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".