Design \nResearch \nfor \nArchitecture: \nUnderstanding \nthe \npeople \nyou \ndesign \nfor
Bibliographic record
Abstract
Design research has grown in popularity in industries that require \n \ndeep understandings of the people they design for. One industry that would appear to benefit \ngreatly from design research, but that does not embrace the practice across its industry, is \narchitecture. Design research itself was employed to understand practicing architects – how they \ncurrently obtain design requirements and how they would ideally like to do so – in order to \nidentify value that an approach to ‘design research for architecture’ could provide. Outcomes of \nthis research included the development of principles \n‘design research for architecture’ should be based upon. From these \n \nprinciples, a highly participatory approach – one that includes both users of the designs and the \nclients that commission them – was adapted to provide the foundations for architects to better \nunderstand their users, to innovate \nthemselves, and to strengthen relationships and trust with their clients.
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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.187 | 0.113 |
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".