MétaCan
Menu
Back to cohort
Record W2800813094 · doi:10.55016/ojs/sppp.v11i1.43344

Canada’s Refugee Strategy – How It Can Be Improved

2018· article· en· W2800813094 on OpenAlexaboutno aff
Robert Vineberg

Bibliographic record

VenueThe School of Public Policy Publications · 2018
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeePolitical scienceLaw

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.062
GPT teacher head0.359
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe School of Public Policy PublicationsSame topicMigration, Health and TraumaFrench-language works237,207