Bibliographic record
Abstract
This book is a groundbreaking comparative law analysis of the world's largest and most mature compensation funds, impacting nearly twenty-two million people in the four jurisdictions of Victoria (Australia), Québec and Manitoba (Canada), and New Zealand. These funds operate in a way that turns tort law on its head, are financially stable and sustainable, and are a true revolution in private law. The author analyses and provides solutions for the core unresolved problems in the field of no-fault compensation and identifies the operational and further development principles of the four largest no-fault compensation funds within these jurisdictions. The similarities and differences between thematically equivalent schemes in civil law countries are examined and the human rights intersections of large no-fault compensation funds are analysed, something which has never been undertaken before in both literature and practice. Based on qualitative surveys of the four funds, the author analyses the funding, quantum of compensation and dispute resolution issues. The book goes on to identify realistic development goals for the existing funds and focuses on the future by identifying new applications used by large no-fault compensation funds for artificial intelligence and emergency public health liability challenges. In particular, this book examines the no-fault compensation funds underpinning the World Health Organization's COVAX scheme, which was established in early 2021. A Comparative Law Analysis of No-Fault Comprehensive Compensation Funds provides valuable new insights for academics, practitioners, policymakers and students in both common law and civil jurisdictions.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".