Negotiating Flexibility with Security in Los Angeles’s In-Home Supportive Services
Bibliographic record
Abstract
This chapter focuses on California's In-Home Supportive Services (IHSS). At the labor market level, both the Direct Funding Program (DF) in Ontario and the IHSS gave “consumers” the flexibility to hire their own “providers,” yet in IHSS the state was more involved in the employment relationship because it paid the provider rather than giving funding directly to the consumer. Many elderly IHSS consumers hire family, but when family is not available, immigrant seniors hire others from their language and ethnic group, and this goes for Pilipinx. Like in DF, labor market flexibility shaped negotiations in the labor process, but in IHSS it shaped it differently. While DF self-managers forged and embraced a friendly employment relationship, consumers in the IHSS context of paying family or co-ethnic fictive kin were more ambivalent about their employer role and used family ideals and family-like practices to negotiate possible tensions at the intimate level. The state's reliance on filial duty and ethnic community through IHSS may bolster flexibility and security at the intimate level in terms of mutually respectful negotiations of what is done, when, where, and how. Yet, as suggested in the previous chapter, collective backing is also important if the goal is flexibility with security. Indeed, another difference between DF and IHSS is that IHSS providers have a union.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".