Settling In: A Comparison of Local Immigrant Organizations in the United States and Canada
Bibliographic record
Abstract
This article examines the effects of national policies and institutional contexts on local immigrant organizations in US and Canadian cities. Drawing on Goldberg and Mercer’s comparative framework, the analysis traces the impacts of three factors on immigrant settlement organizations: divergent national immigration and integration policies, subnational roles, and traditions and understandings of racial and ethnic diversity. Drawing on case studies of Ottawa, Ontario, and Newark, New Jersey, the article illustrates two quite different settlement sectors. A professionalized and federally funded set of non-governmental organizations in Ottawa provides an array of settlement services to newcomers, whereas the Newark sector includes a wide range of organizations from volunteer to professionally run, which carry out activities ranging from legal and political activism to service provision. Formal and informal partnerships mark Ottawa’s settlement sector, whereas collaboration is infrequent and ad hoc in Newark. In Ottawa, a politics of bureaucratic consultation with diverse groups contrasts with a competitive electoral race-based politics in Newark. This study suggests that divergence marks Canadian and American cities at least in the policy arena of immigrant settlement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".