Comparison of design education documents and the disconnect between designer priorities, tools, and occupant assumptions
Bibliographic record
Abstract
While low-level physiological human-factor design strategies have long been discussed in the literature, these design methods are infrequently seen in architecture education and licensure requirements – leaving designers to think about future occupants on their own. In this paper, we study underlying causes of perceptions–and misperceptions–as to the role human factors play in the design process. We present findings from a large-scale textual analysis supported by two studies: (1) building users assume a higher integration of human factors in design tools than how designers perceive the integration and (2) designers place higher importance on less tangible design concepts than building users. Our findings suggest design tools that can augment the knowledge of designers with respect to human physiology and crowd simulations are pertinent to current workflows. We also infer there are likely additional important-to-explore disconnections between users and designers.
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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 | 0.073 | 0.199 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".