Across the Pacific Divide: Latin American Experiences of Integration and Well-Being in Canada and Australia
Bibliographic record
Abstract
There has been a paucity of comparative research regarding social inclusion for the Latin American diaspora worldwide, and this is particularly the case for scholarly work that connects countries in the Americas with nations across the Pacific. Rather than recognise the growing number of Latin Americans who travel throughout the regions of the Pacific Rim, scholarship has remained heavily dominated by works relating to the Latin American population in the United States of America and Europe. In this paper, we shift the attention to Canada and Australia, where both countries increasingly interrogate what have historically been comparable models of multiculturalism and interculturalism. Drawing on data from more a survey of 1600 respondents, we argue that personal and social ties are key to cultivating positive post-migration subjective well-being. In so doing, we highlight the effects of subjective well-being and barriers to integration and belonging. We further illustrate the implications of the intersections of gender, class, and ethnicity in relation to migrants’ well-being and inclusion. Such research demonstrates the need for a better contextualised understanding of migrant inclusion, and for detailed analysis of the situated relationships that constitute Latin American migrant identities across the Pacific Rim.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| 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".