Bibliographic record
Abstract
Peter Showler. Refugee Sandwich: Stories of Exile and Asylum. Montreal and Kingston: McGill-Queen's University Press, 2006. 235 pp. $27.95 sc. There has long been a striking discrepancy between controversy that surrounds treatment of asylum seekers in Canada--especially by Immigration and Refugee Board (IRB), independent, quasi-judicial body that hears claims for protection against persecution--and paucity of works that add to our understanding of this policy area. Given his credentials, author of Refugee Sandwich would appear to be very well-positioned to make an important contribution in this respect, and he does so in a most unexpected and marked way. Showier has worked as an immigration and refugee lawyer, been both a member of IRB (1994-99) and its chairperson (1999-2002), and currently teaches immigration and refugee law at University of Ottawa. Through these various experiences he has seen most every aspect of refugee determination in Canada at close quarters. However, rather than write an analysis of that process or a memoir of his experiences working within it, he has instead created a unique insider's account in guise of a collection of short stories aimed at illustrat[ing] profoundly difficult process of communicating experience of refugees within a judicial context, a difficulty that is shared by all participants (xv). In taking this approach, Refugee Sandwich challenges any simplistic assessment of work of IRB, which tends in public discourse to be condemned for being either too generous or too cruel, and stimulates public debate on how effectiveness and fairness of refugee determination could be increased. The bulk of book consists of a baker's dozen of tales, fictionalized composites that draw on Showler's own experiences, which individually and collectively explore numerous complex ethical and procedural questions that can arise in deciding who deserves Canada's protection and who does not. Although high-stakes nature of refugee determination readily lends itself to a dramatized style, effectiveness of this approach depends on author's ability not only to present a compelling and informed narrative, but also to lead readers themselves to experience the great difficulty and sometimes impossibility of deciding refugee claims accurately (210). And in this, Showler excels. Consider, for example, Excluding Manuel. Manuel is a refugee claimant suspected of complicity in crimes against humanity while serving in police force of an unnamed country. His case has already been heard once but due to inability of Board and Minister to even approximate compliance with their own procedures (20), his initial rejection has been quashed by courts; seven years after his arrival in Canada, his case is about to be heard again. …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".