TACTILE INEQUALITY: NEIGHBOURHOOD SES AND THE FREQUENCY OF CARING TOUCH IN LATER LIFE
Bibliographic record
Abstract
Touch is an important element of human social interaction. Scholars from a variety of disciplines increasingly recognize that supportive physical contact supports health and maximizes older people’s sense of well-being. The investigation of touch, however, has yet to penetrate the field of neighborhood-effects research. Left unexamined, for instance, is whether contextual conditions are associated with patterns of person-to-person contact above and beyond person-level explanations. Drawing from ecological theories in the Chicago school tradition, the present study proposes that neighborhood socioeconomic disadvantage erodes local social ties and cohesion. We hypothesize that these dynamics should produce gaps in supportive touch activity between senior residents of advantaged and disadvantaged communities. Data come from Wave 1 (2005–2006) of the National Social Life, Health and Aging Project (NSHAP), a nationally-representative study of adults age 57–85 (n=3,005). Neighborhoods were defined at the level of census tracts and NSHAP survey data were linked to records from the U.S. Census. Multivariate ordinal logistic regression analyses indicate that seniors living in neighborhoods characterized by low education, low income, high poverty, and high public assistance reported the lowest levels of supportive contact, net individual-level socioeconomic status, personal network size, health, and other covariates. At the same time, seniors who lived in predominantly Black neighborhoods were more likely to experience frequent touch, net other characteristics. Findings suggest a previously unrecognized form of environmental inequality experienced by older adults—tactile inequality—while also pointing to the importance of sub-cultural variation in touch related to racial/ethnic neighborhood composition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".