Seeking refuge: rethinking Canadian settlement policies and programs, and how to imporve access to housingfor refugee claimants in the City of Toronto
Bibliographic record
Abstract
"In recent years, there has been a significant increase in the number of people applying for refugee status, from within Canada or at the border, rising from around 15,000 claims to over 50,000 claims each year. The reasons for this growth are difficult to know, but geopolitical trends and recent changes in refugee policies in the United States have undoubtedly had an impact on the increase in refugee claimants coming to Canada. Refugees face many challenges related to physical and emotional trauma, uncertain immigration status, and financial insecurity. A positive and supportive settlement process upon arrival in a safe country is extremely important. For the most part, in Canada, refugees come to this country through a sponsorship program that involves applying for refugee status before arrival. Refugee status is confirmed, and when the refugees arrive in Canada, financial and social assistance as well as certainty in the immigration process are provided. For asylum seekers who apply for refugee status upon arrival in Canada, usually termed 'refugee claimants', supportive services and certainty of immigration status are not granted at first. With no defined or official program in place at the federal or provincial levels, the responsibility of supporting the settlement of refugee claimant households has by default fallen to municipal shelters and non-profit organizations, causing unsustainable pressure on local services and funding. Access to services and adequate housing can be limited, increasing the vulnerability and uncertainty experienced by refugee claimants in Canada. In this study, key-informant interviews and policy research have illustrated that there have been a variety of consequences of having no dedicated supports or programs for this population. There have been growing tensions regarding which level of government should cover the costs of refugee settlement, challenges for refugee claimants in accessing important settlement and immigration information when they arrive in Canada, as well as an overeliance on municipal shelters and nonprofit organizations to provide settlement support services and initial temporary housing. Complicating this situation, most refugee claimants have settled in only a few municipalities in Canada, leading to an uneven distribution of the costs of providing these supportive services. Specifically, the majority of refugee claimants have come to Toronto, Ontario due to it being a major arrival city for immigrant populations and it having a high concentration of employment opportunities. Unfortunately, the city is in the midst of a housing crisis with few affordable housing options. Many refugee claimants are relying on non-profit organizations and shelters, potentially displacing the existing homeless population. Indeed, as of 2018 over 40% of those using the shelter system were refugee claimants. This additional demand on shelters has led to the need to use short-term emergency measures, such as contracting hotels and student dormitories to be used as additional family shelter space. In general, the responses have been reactionary and expensive, while providing only temporary solutions with limited lasting community benefit. After examining the trends in immigration, the roles of stakeholders and their responses to this increase in refugee claimants, this report examines several potential strategies that could assist with improving the supports available to refugee claimants and help reduce stresses on local service providers when demand increases. In the short term, federal and provincial funding is needed to relieve the added pressure on the City and non-profit organizations that provide temporary shelter and supportive services. In the longer term, there should be a focus on investing in housing options that allow people to exit the shelter system, having the added benefit of producing lasting affordable housing infrastructure in Toronto. Strategies could include protecting existing affordable units and expanding the housing typologies on shelter sites to include transitional and non-profit affordable family housing. These interventions will have the long-term benefit of improving the ability of the city to respond to sudden changes in demand for housing as there would be less overall strain on the system. By taking a long-term approach that invests in community infrastructure rather than focusing on short-term emergency responses, government and civil society stakeholders can develop strategies that support improved access to housing in a more adaptive housing ecosystem that can respond to the changing needs of society. "@eng
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 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".