Issue 11: Scaling Canadaâs Local Immigration Partnerships (LIPs) Model for Proactive Refugee Resettlement
Bibliographic record
Abstract
In this issue of Policy Points we provide a vision for scaling up Canada’s Local Immigration Partnership (LIP) model for refugee resettlement abroad. Global refugee resettlement is an issue that needs a coordinated and collaborative approach that includes communities as partners. Canada presents a proactive and responsive solution to this problem. First introduced in Ontario in 2008, LIPs are a community-based collaborative model for newcomer resettlement and integration that has proven successful in many local communities across Canada. Most importantly, LIPs played an important role in the resettlement of Syrian refugees in several communities across Canada in 2015-2016. The recommendation in this brief aims to offer details to scale up LIPs, a Canadian model of local community involvement in refugee resettlement for the international community.
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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 | 0.029 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.026 | 0.022 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".