Analysis of Core Problems in the Classification of Compensation Funds
Bibliographic record
Abstract
INTRODUCTION This chapter analyses some of the core problems afflicting the classification and analysis of compensation funds generally, and no-fault comprehensive compensation funds specifically. The inconsistent legal philosophical and technical definitions and usage of compensation funds across different jurisdictions illustrate a number of key problems that require proper framing and analysis. THE DISTINCTION BETWEEN SOCIAL SECURITY SCHEMES AND NO-FAULT COMPREHENSIVE COMPENSATION FUNDS The design of social security systems globally varies widely depending upon legal tradition and other political and economic influences. European systems in states like Belgium, the Netherlands and France generally have a ‘Bismarck-ian’ contributions-based social insurance structure. The United Kingdom, New Zealand and Scandinavian countries generally follow a ‘demogrant’ system, which guarantees universal entitlement to social security benefits if citizenship or residency requirements are met. Other countries, including Canada, Australia and the United States take a blended approach involving social insurance, universal social security and private insurance/out-of-pocket principles depending on the particular social security purpose. There are differing theories from legal scholars about the social security overlap with compensation funds generally and no-fault comprehensive compensation funds in particular. A no-fault comprehensive compensation fund has been described by Cane and Goudkamp as a ‘social welfare solution’ to the problem of compensation. Some tort law scholars have classified no-fault comprehensive compensation funds as a complete social insurance system that follows from the logical extension of loss distribution to all accidental losses. Knetsch classified compensation funds generally as being distinct from social security because they provide compensation without means-testing, evidence of prior contributions or affiliation and operate in a narrow field related to the circumstances of damage. Macleod and Hodges have most recently described an international trend towards compensating personal injury on a no-fault basis as a shift to compensating on a ‘wider social basis’ that may also (depending on the jurisdiction) be infused with concepts of social solidarity.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".