Improving Claims Resolution: Alternative Processes in Canada's Immigration System
Bibliographic record
Abstract
This thesis argues that alternative dispute resolution processes form a vital part of Canada's immigration and refugee claims determination system. Using an analytical framework that draws on dispute resolution and relational feminist theory, it explores how alternative processes provide advantages over adversarial ones for claims that engage issues of power and relationships. By aligning claims with appropriate processes, system administrators can improve the fairness, efficiency and durability of resolutions. Introductory Chapters describe the administrative law structure that governs immigration and refugee claims in Canada, and the Immigration Appeal Division's Early Resolution program. This unique initiative integrates alternative processes into the Immigration and Refugee Board of Canada's existing appellate structure. Subsequent Chapters assess how this initiative fares against the relevant scholarship. Strengths and challenges of the current system are highlighted. Concluding sections demonstrate how enhancements to the (i) accessibility of information; (ii) clarity regarding the roles and responsibilities of system actors; and (iii) flexibility in the breadth and depth of available alternative process options, can improve the experience of system users.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.044 | 0.017 |
| Scholarly communication | 0.032 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".