Unpacking ‘birth tourism’: incidental citizenship and the diverse migration and reproduction trajectories of nonresident mothers in Canada
Bibliographic record
Abstract
Birth tourism in Canada is defined as the practice of giving birth on Canadian soil while on a nonresident visa for the express purpose of enabling the child to obtain Canadian citizenship. Proponents of restricting territorial birthright citizenship in Canada portray birth tourism as an illegitimate form of citizenship acquisition, and as a threat to the institution of Canadian citizenship. Yet, very little is known empirically about the motivations of people who are labelled as ‘birth tourists’. Analyzing 49 in-depth interviews with nonresident mothers who gave birth in Canada, we argue that this population is reductively, inaccurately characterised as strictly instrumental citizenship seekers. Our findings reveal that mothers had diverse migratory and reproductive motivations and experiences that ranged from ‘strategic’ to ‘accidental,’ and that giving birth in Canada was often what we term ‘incidental’ to other personal and professional aspirations pursued across international borders. Proposals to thwart birth tourism by abolishing unrestricted jus soli citizenship therefore risk excluding a diverse cross-section of migrants who have a range of reasons for having families in Canada, including those who may end up forming meaningful, enduring attachments to Canadian society. This article thus denaturalizes the myth of the deserving, 'natural-born' citizen.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".