Bibliographic record
Abstract
This study examines the relationships among friends and ethnicity of older adults. Friends includes friend numbers and their quality of relationships with friends of older adults in the current study. Data from the National Social Life, Health, and Aging Project (NSHAP) survey (Waite et al., 2020) were used. The NSHAP study sampled persons 57-85 years of age (n=3005). The respondents completed a telephone survey in which they reported their background information (e.g., income, gender, race, age, health, retirement status, and marital status) and social network characteristics. It was hypothesized that older adults’ ethnicity differentially influenced family relations. In comparison to Anglo older adults, African and Hispanic older adults have weaker (smaller number and less cohesive) family culture. In order to identify the associations between ethnicity and friend relations, multiple regression analysis was used. Results revealed that African American and Hispanic older adults reported larger numbers of close friends, higher quality of friend in general, and higher frequency of contact with them compared to Anglo older adults. The current study’s findings build on a convoy model to account for how older adults’ ethnicity is differentially associated with their quality and size in friend relationship for future research is to examine more diverse in friend and ethnicity variables which explain the dynamic relationships between older adults’ demographic factors and friend network.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".