How Do Organizations and Social Policies ‘Acculturate’ to Immigrants? Accommodating Skilled Immigrants in Canada
Bibliographic record
Abstract
While the idea of acculturation (Berry 1997) was originally proposed as the mutual change of both parties (e.g., immigrants and the host society), the change processes of host societies are neglected in research. A grounded theory study explored the efforts of human service organizations to 'acculturate' to an increasingly diverse immigrant population, through interviews conducted with service providers serving Mainland Chinese immigrants. Acculturation efforts of human service organizations (mezzo-level acculturation) were often needs-driven and affected by the political will and resultant funding programs (macro-level forces). Even with limitations, human service organizations commonly focused on hiring Mainland Chinese immigrants to reflect the changing demographics of their clientele and creating new programs to meet the language and cultural backgrounds of the clients. To contextualize these organizational efforts, an analysis of how policy changes (macro-level acculturation) interact with organizational practice is presented. Finally, the meaning of acculturation for the host society is discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".