Virtual Hearings Circle: Report 2025
Bibliographic record
Abstract
Most refugee hearings in Canada are now held virtually and will be “for the foreseeable future” (IRB 2021). In investigating the consequences of this shift, the Virtual Hearings Circle (VHC) project did not seek to weigh the benefits and drawbacks of the virtual format. Instead, it sought to understand better certain potential risks. The VHC project brought together twelve participants for a day-long discussion: two former refugee claimants; three refugee lawyers; two members of refugee serving agencies; and five academics with related areas of study. The aim of this discussion was to understand whether the virtual hearing format increases the risk that a refugee claimant will be misunderstood or wrongly disbelieved, or the risk that they will have an acutely stressful experience – and if this format increases either of these risks, to understand under what circumstances, how and why. Using a talking circle methodology, the VHC participants shared their observations about how the virtual format affects claimants’ testimony, counsel and interpreters’ work, and decision-makers’ judgments and identified areas of concern.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.048 |
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".