When Moral Hazard Is Good: A Critique Of The United States Health Insurance System
Bibliographic record
Abstract
Health plays an immensely significant role in an individual's life; it can determine both the quality and the length of a person's existence. It is therefore no surprise that health care is such a prevalent and controversial topic in the world today. In contrast to the historically public and non-exclusive health insurance in Europe, the health care system in the United States is exclusive and relies heavily on the private market for financing (Geyman 2005). This American preference toward consumers-directed health care is explained by the controversial "moral hazard" theory of health insurance. According to this theory, full insurance encourages individuals to overuse health services because they appear "free" or highly subsidized. New theory, however, indicates that this dominant view of moral hazard is fundamentally flawed, that full insurance is in fact effective and efficient.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".