Bibliographic record
Abstract
When citizens lose faith in their government’s refugee policies, there arises the potential for an anti-immigration backlash, as several European countries have recently discovered. Canada has yet to see that happen, but it has for too long been muddling along with a refugee-processing system that is seriously flawed. Refugees go unprocessed for years, and in the meantime end up living, working and laying down roots. Often that only increases the chances they will end up staying even if they might have otherwise been rejected. It may even lead to increases in questionable refugee claims, as people realize they can work and make money in Canada for years before their case is even heard. The Canadian government has committed to increasing refugee numbers. The United Nations High Commissioner for Refugees has designated Canada as the primary destination for hard-to-settle refugees. The diversity of source countries is increasing, resulting in more refugees who are illiterate in both English and French. More refugees will struggle to adapt to life in Canada. Taken together, it is possible that without fixing the problems in the system, public dissatisfaction could rise as Canadians lose faith that their refugee system is under control, and that could undermine their faith in the entire immigration system. The biggest flaw in the refugee system traces back to the government’s overreaction to the Singh decision. The Supreme Court ordered that all rejected refugees had a right to an in-person appeal, but the federal government went much further and gave every refugee an in-person hearing. That system has left Canada with a backlog, as of last year, of 34,000 cases. In most every other country, initial refugee screenings are conducted by public servants working for the immigration agency, which here would be Immigration, Refugees and Citizenship Canada, as opposed to the staff of the Immigration and Refugee Board. Canada could do a much better job at clearing its backlog and better processing refugee claims, particularly in weeding out the bogus claims, by reassigning responsibility for interviewing refugees to the officials at IRCC. The agency also has the advantage of having offices in almost every major city in Canada, while the IRB only hears cases in Vancouver, Montreal and Toronto, with the 2,500 refugee claimants residing in places outside B.C., Quebec and Ontario having to travel long distances for a hearing or having to settle for an inferior two-way video hearing. Also, this would avoid the conflict of interest at the IRB, which is in charge of reviewing appeals of its own decisions. The IRB is better suited to handle appeals of decisions made by IRCC agents who are at arm’s-length from the IRB. In addition, Canada should also ensure it maintains a balance in accepting not strictly refugees who are most at risk, but also an equal number of refugees who will more easily establish themselves in Canada and adapt within one year of landing in Canada. Having a system that not only ensures more efficient, effective processing of refugees, with proper control over who settles here, will not only help Canadians maintain confidence in their refugee and immigration system. It will also ensure that Canada has a system that can respond, when necessary, to global crises when they erupt, and better help those refugees who need protection.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".