Human Rights, Access to Justice and Dispute Resolution
Bibliographic record
Abstract
INTRODUCTION A no-fault comprehensive compensation fund is a significant part of a jurisdiction’s legal framework. In the four jurisdictions that are studied here, the fund framework affects the way that individuals access justice for specific types of injury-related harms. Most specifically, it restricts access to the courts and an individual’s choice about whether to pursue potential defendants. However there has been little or no analysis of how no-fault comprehensive compensation funds intersect with human rights and access to justice issues. There is a current knowledge gap about the intersection between human rights and no-fault compensation funds generally. This chapter, however, focuses on the impact of comprehensive no-fault funds on human rights and access to justice issues, as a discrete sub-field. Large funds pose slightly different issues in the realm of human rights, compared with their smaller cousins. This is because they create a near or total restriction on the ability of a comprehensive category of claimants to access court-based remedies or settlements. As a trade-off for this, they offer a statutory entitlement to compensation for certain classes of injury, and a fast and streamlined claims process. However, a question that has not fully been analysed by research or practice remains: is this balancing act ultimately a fundamental problem from a human rights perspective, or an acceptable alternative approach? The four big funds studied in this book are located in jurisdictions that do not all have a fundamental human rights framework equivalent to the European Convention on Human Rights (ECHR) or the United States Constitution. Only Canada’s human rights frameworks are connected to its written constitution and/or have a fundamental nature. This leads to a natural or default presumption that a fundamental human rights framework may be incompatible with such a fund, or that the fund hinders access to justice in an unacceptable way. However, there has been no scholarly legal analysis of whether these presumptions or suspicions would be correct.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 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".