Visual Anthropology and Public Design: Can the Association Between These Fields Generate Valuable Insights Into the Diverse Patterns of Urban Behaviour?
Bibliographic record
Abstract
Understanding the users’ needs in public spaces is often a challenge to industrial designers. Cities are growing fast and urban spaces should be adapted to these changes. This paper probes the utilization of visual anthropology theories and methods as tools to support the interpretation of the city dwellers’ diverse behaviours. Adaptations and interventions performed by the public are regarded as hints of their desires, which should be fulfilled by the urban elements, that is, the products that are placed in urban spaces. In order to verify such assumptions, a cultural inventory was conducted in selected public spaces in Ottawa, Canada. The researcher looked for material evidences of modifications to urban products made by the population and documented them in photographs. The images provided the study with useful data, whose analysis provided possible insights into public design development.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.055 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".