THE SENIOR COMMUNITY SERVICE EMPLOYMENT PROGRAM: NEW DATA ON OLDER ASIAN WORKERS
Bibliographic record
Abstract
Abstract The Senior Community Service Employment Program (SCSEP) is the only federal employment program for people aged 55 and older. While 6% of the approximately 60,000 participants are Asian and federal funds are set aside to serve Asian workers, little is known about their personal characteristics and experiences in SCSEP. Using novel data from an online and paper-based Massachusetts survey of SCSEP participants from April to August 2022 offered in multiple languages, this study aimed to describe Asian participants’ characteristics and experiences in SCSEP. Respondents (Nf39) ranged in age from 58 to 73. Almost all spoke a language other than English at home, and all respondents were born outside of the U.S. Nearly half reported a high school degree or less, and none reported “excellent” health. One-third reported feeling lonely occasionally or in specific situations. While two-thirds have made recent tradeoffs in paying for important goods and services (e.g., food and health care), most reported that their personal finances, social engagement, family life, and self-confidence have improved due to SCSEP. Further, 9 in 10 agreed that their supervisors understood the goals of SCSEP and were supportive. A quarter said separately that it was very likely they would search for a paid job or volunteer role after exiting SCSEP. These results contribute to the small amount of literature on older Asian American workers while informing the work of organizations that serve older Asian workers who have experienced multiple barriers to employment relating to nativity status, language abilities, and anti-Asian discrimination.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".