Pensions: Strengthening Employer-Based Health Care
Bibliographic record
Abstract
thank you for inviting me here today to address important issues related to employerbased health insurance. Employers in the United States face significant constraints on profitability due to rising health insurance costs. Many of these costs are well known: National health expenditures reached a record high last year: $2.4 trillion, about $7,900 per person. 1 A quarter of our nation’s health spending is supported by businesses. The largest share of that spending – 77 percent – is employer contributions to health insurance plans for their employees. In 2007, businesses spent a total of $518 billion dollars on health services: $398 billion in employer contributions to private health insurance premiums, $82 billion in contributions to the Medicare Hospital Insurance Trust Fund, and $38 billion to workers ’ compensation, Testimony as Prepared for Delivery 2
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.050 | 0.014 |
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".