Municipal newcomer assistance in Lloydminster: evaluating policy networks in immigration settlement services
Bibliographic record
Abstract
This research explores the value of the federally funded Local Immigration Partnership (LIP) program as the initiative expands from communities in Ontario to communities across Canada. While labour market demands make the recruitment and retention of immigrants a serious policy problem in smaller, more isolated centres, the existing academic literature has highlighted the importance of local settlement support services. In smaller centres, these services, if they are available, are delivered by a range of federal, provincial and municipal government agencies, acting in partnership with a range of Non-Governmental Organizations. There has been concern that there is a lack of cohesion in this policy network, which is particularly problematic given the network’s vital role in delivering services. Academic research indicates that relevant community actors are not sufficiently connected on immigration issues, and the LIP program has been designed as an information-based policy instrument, providing funding to help organize networked service delivery more effectively – this is seen as a low-cost strategy for improving immigration support in smaller cities. Regions of Canada vary in their strengths and in their challenges, and the LIP program’s focus on enhancing existing immigration-sector networks seeks to account for these differences. This project presents a case study of Lloydminster, Alberta/Saskatchewan to test the potential applicability and receptivity of the LIP program in a rurally located, economically booming, small Western Canadian city. Lloydminster’s immigration-sector network has improved on its own over time; organizations on the periphery still feel disconnected, which is negatively impacting immigrant integration. From the data collected through this project, it is clear that a Local Immigration Partnership has the potential to improve the network in Lloydminster, and that the relevant community actors see real benefits in this approach to immigration policy.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".