Valuing Care: Policies and Practices to Advance an Equitable and High-Quality Care Economy
Bibliographic record
Abstract
Care is the invisible infrastructure that sustains our societies and economies. Every one of us has needed care in the past, and all of us will rely on it again as we age. Many also provide care, whether for children, elders, or others in need, often at great personal and economic cost. Yet, care remains undervalued, underfunded, and overlooked in public policy, even though it underpins our communities and drives economic productivity. At a moment of demographic change, global inequality, and rising demand, investing in care is not only a moral imperative but also an economic necessity. The research covered in this report suggests several policy implications for governments and employers in creating more equitable, high-quality, and resilient care systems. A focus on improving care systems will improve outcomes for care recipients as well as the caregivers who support them. Policy can aim to ensure that everyone has access to high-quality care, especially those belonging to marginalized communities, that carers are working in fair conditions with sustainable wages, and that future trends relating to migration, aging populations, technology, and climate change are key considerations.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.056 | 0.022 |
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".