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
This article uses data obtained from Canada’s Immigration and Refugee Board [IRB] to calculate the refugee claim grant rates of individual IRB adjudicators.The data reveals that, in 2006, grant rates varied significantly across adjudicators.Some adjudicators accorded refugee status in virtually all cases they heard, others granted refugee status rarely, if at all. The article explores several explanations offered by the IRB for refugee claim grant rate variations. These explanations relate to patterns in case assignment due to adjudicator specialization in particular types of cases from particular regions of the world. The author contends that while patterns in case assignment do affect grant rates, they do not account for the full variations evident in the data. Rather, outcomes in refugee adjudication appear to hinge, at least in part, on the identity of the adjudicator assigned. The author draws three main conclusions from the data on refugee adjudication in 2006. First, further empirical research should be undertaken to verify the results of the study and to identify specific features of adjudicator identity that affect refugee claim outcomes. Second, the appointment process for IRB adjudicators should be carefully scrutinized in light of grant rate disparities.Third, given both the grant rate disparities and the life and death stakes involved in refugee adjudication, it is imperative there be opportunities to meaningfully review negative first instance refugee determinations. To this end, the government should immediately implement the provisions in Canada’s immigration legislation that establish a Refugee Appeal Division at the IRB.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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