Review of Existing No-Fault Comprehensive Compensation Funds
Bibliographic record
Abstract
INTRODUCTION This chapter considers the four existing no-fault comprehensive compensation funds, and analyses their purpose, scope and effect. Firstly, each jurisdiction is described and analysed using a pluralist comparative law approach (considering functional, structural, and in-context elements). After this, the selected funds are compared and analysed by their function – how they deal with the same kinds of loss incidents (traffic injury, medical/pharmaceutical, employment injury and everyday risks of life). Next, the learnings from the no-fault comprehensive approach are compared to functionally relevant French, Belgian and Nordic no-fault compensation funds. The European funds have functional and purposive relevance to our four no-fault comprehensive compensation funds. They are not as comprehensive as the key New Zealand, Australian and Canadian funds, but they represent a significant departure from standard compensation and liability law approaches in those jurisdictions. The European funds have also been compared in the past with one another. Finally, conclusions are drawn about this comparative law analysis of existing systems. This partially answers the first and third key questions, and forms the departure point for further comparative law analyses in the subsequent chapters on issues of administration, human rights, access to justice and possible future applications. NEW ZEALAND’S ACCIDENT COMPENSATION CORPORATION (ACC) When jurists consider the issue of tort law losses, social insurance protections and insurance coverage in the New Zealand jurisdiction, the impact of one compensation fund is always addressed before any other consideration. That fund is the universal single-window statutory scheme for personal injury compensation, administered by the Accident Compensation Corporation (hereafter ‘the ACC Scheme’). Under this scheme, individuals forego the right to sue for compensatory damages for injury under the common law, in exchange for this comprehensive cover and compensation. The fund also has a mirror injury prevention and rehabilitation focus, which makes the purpose and scope of the scheme wider than financial compensation. The ACC Scheme is something of a legal ‘unicorn’ because, despite being in effect for the best part of 50 years, it has not been wholly replicated (i.e. its multifunction coverage) in any other major common law or civil law jurisdiction.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".