Physical Spacing and Social Interaction Before the Global Pandemic
Bibliographic record
Abstract
Subsequent to the arrival of SARS-CoV-2 and emergence of COVID-19, policy to limit the further spread has focused on increasing distance between individuals when interacting, often termed social distancing although physical distancing is more accurate (Das Gupta and Wong in Canadian J Public Health 111:488-489, 2020; Gale in Is 'social distancing' the wrong term? Expert prefers 'physical distancing,' and the WHO agrees. The Washington Post, 2020; Sørensen et al. in Glob Health Promot, 28:5-14, 2021), and limiting the frequency of interaction by limiting/prohibiting non-essential and large-scale social gatherings. This research note focuses on social spacing, defined by distance and interaction, to offer a cross-cultural insight into social distancing and social interactions in the pre-pandemic period. Combining unique data on frequency of contact, religious service attendance and preferred interpersonal spacing in 20 countries, this research note considers variation in the extent to which physical distance was already practiced without official recommendations and underscores notable cross-cultural variation in the extent to which social interaction occurred. Results suggest that policy intervention should emphasize certain behavioral changes based on pre-existing context-specific patterns of interaction and interpersonal spacing rather than a one-size-fits-all approach. This research note is a descriptive first step that allows unique insight into social spacing and contact prior to the spread of SARS-CoV-2. It provides a baseline typology and a reference for future work on the cross-cultural implications of COVID-19 for pre-pandemic socio-cultural practice and vice versa.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".