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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.009 | 0.011 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.036 | 0.017 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.017 |
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; both teacher heads agree on what is shown here.
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".