Investigating expert and lay judgments of pathogen transmission risk in urban and architectural environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The threat of infection posed by the COVID-19 virus forced the general public to use the visible cues within urban and architectural environments as a ‘lens’ through which pathogen transmission risk could be inferred. This study presents a model that quantifies associations between (1) the visible cues of urban and architectural environments and lay ratings of pathogen transmission risk, (2) the same relationship for experts (i.e. Infection Control Practitioners), and (3) the association between the lay ratings and the expert ratings. A series of urban and architectural environments were rated on twenty visible architectural cues and for their perceived pathogen transmission risk by lay and expert raters. Correlational analyses between the two groups yielded considerable consensus between risk ratings, as well as between which cues were significantly associated with risk ratings, which included the space’s crowdedness, the potential for crowds, and cleanliness. Expert risk ratings were also significantly associated with corridor size, and marginally significantly associated with the number of touchable surfaces, the number of furniture/seating, and access to fresh air. In this way, expert cue utilization is more complex than lay assessments. Implications for public health policy makers and designers of the built environment are discussed.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it