A Systems Perspective on Canadian \nImmigration
Bibliographic record
Abstract
Canada relies on immigration for it s future prosperity. Its population is not growing fast enough to replenish the large number of workers set to retire and this means that the country cannot maintain its economic status nor can it develop and advance. Immigration is a solution to this problem. Yet, despite years of policy changes designed to improve the immigration system, certain problems continue to exist. Immigrants continue to experience economic and cultural hardships in the settlement phase. Using systems thinking methodology, system mapping and semi structured interviews with several key stakeholders in the immigration system, this study explores how stakeholders interact with each other to produce outcomes that negatively impact immigrant settlement. Using a systems map of stakeholders of varying power and influence, the exploration seeks out points of intervention to improve the immigration system’s efficiency and effectiveness in settling immigrants in Canada. The paper offers overall recommendations for the immigration system and for addressing settlement related problems such as access to settlement services, immigrant employment, culture shock and immigrant stereotypes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".