Bibliographic record
Abstract
The first part of this chapter provides an historical overview of the Canadian asylum process. The second part explores how international and constitutional law structures shapes and constrains asylum law in Canada. The next two sections discuss elements of the asylum regime through an enlarged conception of the right to be heard. In its traditional and limited iteration (audi alteram partem, or ‘hear the other side’), this right is familiar to any student of administrative law in the common law world. A somewhat figurative concept of the right to be heard is used here in order to consider different points in Canadian legal and geographical space where the asylum seeker's audibility is enabled or muted. In particular, attention is drawn to the limits imposed by conventional legal doctrine on what will be, or can be, heard from the other side of a cultural divide, a hearing room, or a border. The conclusion contemplates a separate but related dialogue that has emerged recently, partly in response to the obfuscating noise and troubling silences in the existing regime. Part one: the asylum seeker in the Canadian legal system Canada is a settler society par excellence. Almost 20 per cent of the population is foreign-born, putting it right behind Australia as a country of immigration in empirical terms. Canada is also a country of immigration in the normative sense; official discourse celebrates immigration as constitutive of the nation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".