The Securitisation of Canada’s Refugee System: Reviewing the Unintended Consequences of the 2012 Reform
Bibliographic record
Abstract
In 2012, Canada made regulatory changes and adopted legislations amending the Immigration and Refugee Protection Act, including the Protecting Canada’s Immigration System Act and the Balanced Refugee Reform Act. These pieces of legislation contain a number of measures which include: expedited refugee claim hearings, reduced procedural guarantees and reviews, growing use of socioeconomic deterrents, and increased immigration detention. Drawing on a qualitative research, this article explores the unintended results and counter-productive effects of these new measures, with a particular focus on their practical and human rights implications. It is argued that the government has used the language of security to rationalise the imposition of disproportionately harsh treatment on asylum seekers. Unsurprisingly, the new measures have resulted in violations of asylum seekers’ human rights. In addition, they have had a detrimental impact on third parties involved in the refugee protection system, such as legal counsels and service providers. Finally, it is argued that there is a correlation between the new refugee measures and the increase in irregular migration in Canada.
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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| 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".