No-Fault, Motor Vehicle Accidents, and The Civil Resolution Tribunal: Effective Justice or False Prophet?
Bibliographic record
Abstract
Since May 2021, instead of using tort litigation and lawyers to determine a lump-sum amount for what a person should be compensated for after being injured in a car crash, the Insurance Corporation of British Columbia [ICBC] has shifted to a new No-Fault system that provides statutorily defined entitlement to care, recovery and income replacement benefits. Disputes over those benefits are now under the jurisdiction of the rapidly growing Civil Resolution Tribunal [CRT], an online tribunal lauded for its contribution to access to justice (A2J) as a negotiation and adjudication platform for use without lawyers for smaller and simpler civil matters. This article asks whether the combined shift to No-Fault and CRT jurisdiction effectively meets the access to justice needs of those injured in motor vehicle accidents. After surveying the institutional changes to the A2J landscape for those injured in MVAs, narratives surrounding the changes, and conducting a quantitative outcome of analysis of CRT decisions between individual claimants and ICBC where ICBC was found to be successful before the CRT in 73% of cases, this article concludes that ICBC’s new No-Fault system creates A2J concerns and significant bargaining inequalities for those injured in MVAs that are not overcome by recourse to the CRT. More broadly, this article illustrates the complexities of A2J as a relational and multi-faceted phenomenon in an era where one-dimensional metrics of cost-savings, speed, and efficiency are frequently promoted as novel A2J cures.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.055 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".