Not Just the Facts: Adjudicator Bias and Decisions of the Immigration and Refugee Board of Canada (2006–2011)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Immigration and Refugee Board of Canada (IRB) is Canada's largest administrative tribunal. The Refugee Protection Division (RPD) of the IRB is responsible for the adjudication of refugee claims made in Canada. In accordance with its obligations under international law, Canada grants protection to persons who have a well-founded fear of persecution because of race, nationality, religion, political opinion, or membership in a particular social group. In addition, a person may request protection in Canada on the basis of his or her fear of torture, risk to life or risk of cruel and unusual treatment or punishment. Acceptance (approval) rates of claims vary widely across members of the IRB, with some granting asylum in less than 10 percent of cases, and others granting asylum in more than 90 percent of cases. Despite this fact, there is a lack of analysis exploring whether grant rates vary systematically in relationship to observed characteristics of adjudicators. This paper presents statistical analysis of over 68,000 refugee claims adjudicated by 264 members of the board from 2006 to 2011. It finds that the probability of acceptance is associated with individual members' characteristics including education, gender, and professional experience, when holding constant the claimant's country of origin, gender, and the year and regional office of adjudication. The findings suggest that the identity of the adjudicator affects whether or not an individual receives asylum.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it