Marriage-based Migration and Human Rights Education: Where Does Canada Stand?
Bibliographic record
Abstract
Immigration for marriage is one of the most prevalent forms of population movement from developing to developed nations, particularly for women (Ghosh, 2009). As an industrialized nation with an international reputation for embracing diversity and pluralism, Canada is a country where many individuals from the developing world aspire to establish their family lives. Approximately 30 percent of newcomers arriving in Canada annually are family members sponsored by Canadian citizens or permanent residents, with the majority of them being spouses from abroad (Citizenship and Immigration Canada, 2007). Since the foreign countries from which female marriage migrants have arrived often have different systems of governance and human rights records, the responsibility has been placed on the federal government to educate newcomers about their rights as migrants and their basic human rights (Global Commission on International Migration, 2005). Since Canada’s family sponsorship policy holds male sponsors of immigrant brides directly responsible for facilitating women’s integration and upholding their rights, the government has an equal obligation to educate sponsors about each party’s rights in the sponsorship relationship. This chapter describes the method and results of a content analysis of government issued information for sponsors and sponsored persons and its human rights coverage. It outlines implications for rights-based education targeting both newcomers and their hosts/sponsors in marriage-based immigration cases.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".