âStranger Dangerâ: A Critical Discourse Analysis of the Immigration and Refugee Protection Act
Bibliographic record
Abstract
This paper examines a major federal migrant policy within Canada, the\nImmigration and Refugee Protection Act (IRPA), as a discursive mechanism that I argue perpetuates racializing identities of immigrants and refugees. By performing a Critical Discourse Analysis (CDA) of the IRPA, a number of themes are identified that construct immigrants and refugees in both racializing and securitizing terms. General themes from the Charter of Rights and Freedoms are then used to interrogate these identified themes within the IRPA. I conclude by suggesting that implementing and emphasizing humanitarian discourse within the IRPA can dislodge controversial security measures from operating within a space of legal exception to the Charter.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.027 | 0.041 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".