The influence of architectural detailing, massing, and design interest on the evaluation of heritage and historic urban streetscapes
Bibliographic record
Abstract
Research in environmental perception has illustrated that contextual compatibility and building facade ornamentation are important determiners of preference for specific architectural designs. This study extended these ideas by investigating the perceptions of contextual compatibility between two groups when assessing heritage and historic urban streetscapes in addition to testing the significance of the presence of facade detailing (specifically, quoins and a window treatment), rather than massing, in these evaluations. Participants were divided into students with and without a design interest, and then asked to assess four heritage and historic urban streetscape sketches using a unipolar adjectival rating scale arranged into seven ad hoc categories. A factor analysis yielded six distinct scale groups. Subsequently, multiple analyses of variance were executed, demonstrating that the results did not support the hypotheses, although several main and interaction effects were found. When all four independent variables (i.e., design interest, the two detailing variables, and massing) were included, a window treatment main effect, a window treatment by design interest interaction and a massing by quoins by window treatment interaction surfaced across the six factors. (Abstract shortened by UMI.)
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".