Bibliographic record
Abstract
R efugee Sandwich ought to be compulsory reading for every Canadian member of Parliament, and is recommended reading for anyone who votes in this country. Peter Showler has a careers-worth of experience working in all aspects of Canadian refugee law. It is a tribute to his immense insight that Refugee Sandwich is his chosen contribution at this point, the culmination of well nigh thirty years of reflection. The book goes right to the heart of the central problem of refugee law and policy here and elsewhere: positions on all sides of public discourse are entrenched, no one is learning anything new, innovation is stifled by a need to defend each corner. It is impossible to express any complexity in this atmosphere, let alone shed any light on the labyrinth which is refugee decision-making. Showler is an advocate. At a juncture when many advocates would have written a political tract, led a non-governmental organization, joined a think tank, or published a text, Showler has given us a work of what might be called ‘fiction’. It is a crowning achievement. Refugee Sandwich is principally comprised of thirteen stories told from and about different positions in Canada’s refugee determination process. We are introduced to lawyers and judges, interpreters and decision-makers, bureaucrats, refugees and claimants. Even the much maligned refugee protection officer has a voice. Heroes and villains are largely off-stage. Despotic regimes and genocidaires are condemned, but this is never the focus of the narrative. The people we meet are too complex for easy labels. Each story works its way around a sharp grain of truth, aiming at the oyster’s trick. Some are told in the first person, some with omniscient narration. Every pearl is not evenly formed, but then each bit of truth is not an equally attractive starting premise.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".