Clean-moral effects and clean-slate effects: Physical cleansing as an embodied procedure of psychological separation
Bibliographic record
Abstract
Handwashing is one of the easiest and most effective opportunities to enjoy substantial health benefi ts. Meta-analytic evidence associates handwashing with reduction in risks of diarrheal diseases by 42-47 percent, severe intestinal infections by 48 percent, and shigellosis by 59 percent; extrapolation analyses suggest that handwashing could avert 0.5-1.4 million potential diarrhea deaths (Curtis & Cairncross, 2003). In a randomized controlled trial of 906 households in Karachi, Pakistan, households that were (vs. were not) given handwashing promotion and plain soap showed substantial reductions in childhood incidences of pneumonia (by 50 percent) and diarrhea (by 53 percent). Antibacterial soap worked similarly well (Luby et al., 2005) on these two clinical syndromes that bring about the most childhood deaths around the world, especially in poor communities in developing countries. Similar effects of handwashing promotion on respiratory tract infections and respiratory illness have been found among children and adults in developed countries including Canada, Australia and the United States (Master, Hess Longe, & Dickson, 1997; Niffenegger, 1997; Carabin et al., 1999; Roberts et al., 2000; Ryan, Christian, & Wohlrabe, 2001). The health impact of handwashing is signifi cant enough that October 15 is designated as Global Handwashing Day, when people all over the world are educated about the practices and benefi ts of effective handwashing.
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.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".