Local Immigrant Partnerships in Canada: Navigating Between Macro Teleology and Micro Deontology
Bibliographic record
Abstract
Migration of all forms continues to grow rapidly across the globe. At the same time, the ethics of migration and migration policy is hotly contested. We explore the ethics of immigration in the Canadian context. The Canadian government has funded a new type of entity, referred to as the “Local Immigrant Partnership” (LIP) to help manage immigration. In this paper, we seek to understand this particular organizational form that has evolved in the Canadian context. Using a novel machine learning topic modeling approach, network analysis, and textual analysis, we examine archival documents for extant Canadian LIPs. We supplement these findings with primary data collected from LIP respondents. Our preliminary findings indicate that LIPs are currently primarily seeking legitimacy and trying to define a niche for themselves within the immigration space, so as to gain access to potentially shrinking critical resources. This leads to an ethical tension between the needs of governmental providers of capital and consumers of the services. We conclude by suggesting avenues of future research.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".