The Service Contracts Model Act: a quarter century and counting - what now?
Bibliographic record
Abstract
The U.S. service contract industry operates largely as a regulatory orphan; i.e., an industry without strong, focused, or consistent oversight coming from any particular authoritative body. That said, 26 years ago, the NAIC—the U.S. insurance coordinating regulatory body—stepped into that void when it promulgated its Service Contracts Model Act (#685) as a measure for the regulation of that industry. Since that introduction, the service contract industry has almost doubled in size, and is still growing at a rapid pace, while the industry inefficiencies that initially precipitated the NAIC’s original action persist. Noting the passage of a quarter century, this paper takes the opportunity to reassess the effectiveness of Model #685 and contemplate viable regulatory options for the service contract marketplace. This paper provides a brief summary of the history of the service contract industry in the U.S., efforts to regulate its behavior, and an assessment as to feasible sources of regulation in the future.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".