The Real Meaning of “Managed Care”: Adaptive Accumulation and U.S. Health Care
Bibliographic record
Abstract
The boundaries of what constitutes “sufficient” health have always been open and, as such, health care has proven to be an opportune area for profit growth. In the United States, the allure of health as a market commodity has proven very strong, but even here it cannot be a mere spontaneous product of the market. It requires government to foster and develop public policy that effectively promotes and maintains health care delivery across the population. Historically, U.S. public policy has veered away from anything akin to universal care, and it has typically been understood as an outlier among advanced industrial states. But, simultaneously, it is also the largest health care market in the world, soon to engulf a full fifth of its GDP. In this paper, I argue that the complicated dynamic between a growing market in health delivery and a patchwork of political reforms has encouraged “adaptive accumulation,” a process whereby capital secures optimized accumulation outcomes from enhanced government intervention, deriving extra-market benefits along the way. To make this argument, I explore critical components of the health system, including Medicare Advantage, Medicare Part D, as well as the Affordable Care Act and its aftermath.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".