A study of homelessness and migration in northern rural and urban centres in the Near North, Ontario, Canada, using GIS techniques
Bibliographic record
Abstract
Homelessness, migration and poverty in Northern Ontario, Canada are serious issues. In order to facilitate development of policy that effectively addresses these problems, a long-term Community-University Research Alliance at Laurentian University, Sudbury, Canada was initiated. The present research was conducted on the data gathered through this initiative with a goal of understanding pathways to homelessness in Northern Ontario. Data gathered in five communities (Sudbury, Timmins, Hearst, Cochrane, Moosonee) between the years 2001 and 2012 were analyzed. It was found that these communities, though located in the same province of Ontario, suffered from pathways to homelessness that were different from one another. These differences result from many factors including concentration of different ethnicities in different localities as well as non-uniform availability of education, employment and health facilities. For example, high rate of unemployment in Moosonee results in migration to larger cities such as Timmins and Sudbury, which sometimes leads to homelessness as these cities themselves do not have proper support structures and resources available to help these migrants. An interesting phenomenon observed during the analysis was that there is a trend of individuals migrating out in search of employment, becoming unsuccessful in securing employment, returning their home town and then becoming homeless. This was seen across the board in all five communities, which points to the scarcity of proper support structure and resources. An index of homelessness was also constructed during this study based on the variables that were seen to have the highest impact on homelessness. For this Fuzzy Cognitive Mapping approach was adopted. It resulted in separate equations for homelessness in the five communities studied and indicated the spatial dependence of pathways to homelessness. The main result that has come out of this study is that different communities in Northern Ontario, even though they are not very far apart from one another, have their unique challenges when it comes to homelessness, migration and poverty and therefore a uniform policy across the whole of Northern Ontario will not be an effective way to address these problems. The framework developed in this study to determine if a particular individual is at-risk of becoming homeless can be beneficial in formulating effective policy and strategy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".