Knowing Our History: How the Structural Context of California’s Aging Network Evolved
Bibliographic record
Abstract
In June 2019, Governor Gavin Newsom signed an executive order calling for the creation of a Master Plan for Aging (MPA.) The opening paragraph affirms “California’s commitment to build an age-friendly state so that all Californians can age with dignity and independence.” (California Health and Human Services Agency 2020). The MPA was released in January 2021. I was hired as the consultant MPA Historian to document the chronological sequence of services and to highlight the major strategies California has adopted to serve older adults and people with disabilities. I researched archival documents and interviewed influencers, policy makers, and community based providers. The goal to successfully age in one’s community is, in part, the result of preceding decades of federal and state leadership, implementation strategies and advocacy. The evolution of aging services in California began with robust initiation and expansion in the 1970s but faced near total devastation twenty years later due to severe budget deficits. The approach to addressing aging has been complex since the 1960’s.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".