Bibliographic record
Abstract
The Civil Resolution Tribunal (CRT) is a governmental dispute resolution body for citizens and legal entities in British Columbia, a province of Canada. It replaces the procedure at court for litigants in cases with small claims up to € 3.400, condomin-ium disputes, motor vehicle injury cases, and societies and cooperative association cases. Rules for litigation changed accordingly. For instance, the procedure is conducted fully online when possible, representation is allowed only by exception, and mediation must be attempted before a decision can be rendered. The tribunal includes a digital portal providing persons seeking justice with problem diagnosis, information and self-help tools. The idea behind the tribunal is that it will simplify access to professional dispute resolution. In addition, disputes could be resolved faster, at lower costs, and in a more informal and flexible manner by means of reaching an agreement between parties where possible and providing a tribunal decision if necessary. Whether the system can be beneficial in the Dutch context is, however, not immediately clear. The current research aims to provide insight into the origin of the tribunal and to explore the advantages and disadvantages of implementing a similar facility in The Netherlands. Central to this research are the following research questions. 1 What is the origin story of the CRT-system? 2 What are the characteristics of the CRT-system? 3 What advantages and disadvantages of (elements of) the CRT-system are discussed in legal sciences literature between 2012 and mid 2020? 4 How does the CRT-system relates to the Dutch context?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.205 | 0.084 |
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".