A Federal Bill, With Commentary, to Allow Choice in Auto Insurance
Bibliographic record
Abstract
1) [hereinafter No-FAULT APPROACHES];(2) the coverage is optional, JOOST, supra , at 35; and (3) there are no restrictions on lawsuits in "add-on" states.Oregon and Delaware are different because they require motorists to purchase no-fault benefits and at reasonably high levels.The trial bar's rationale for add-on no-fault was that if people were properly treated by their insurers, they would not sue.Not surprisingly, in most of these states, the people just took the no-fault benefits and then sued, because the incentive to sue-the pain and suffering multiplier, see discussion in the commentary about section 2(1)(A)-was still there.In some respects, the nofault benefits helped finance lawsuits for some who otherwise would have settled because they could not have afforded to wait long enough to sue.As a result of these inherent defects, cost experience in the add-on states has generally been poor.4. Ten states mandate no-fault insurance, while three others-Kentucky, Pennsylvania and New Jersey-are actually choice states, in that they offer a choice between a no-fault system and a tort option with no restrictions on lawsuits.The District of Columbia also has a choice system under which, after an accident, a motorist can decide whether to use tort coverage or no-fault coverage.Unlike H.R. 1704, however, none of these state laws offers drivers a choice to forgo pain and suffering coverage entirely.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.123 | 0.045 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".