‘Birth Tourism,’ Citizenship, and the Politics of Deservingness in Canada: Analyzing Parliamentary and Newspaper Media Discourses from 1990 to 2021
Bibliographic record
Abstract
This thesis examines a phenomenon that has been controversially labeled as ‘birth tourism’ in the Canadian context. Allegedly, pregnant women from other countries are coming to Canada solely for the purpose of giving birth to their children. This is ostensibly so that the child gains Canadian citizenship before returning to the parent’s country of origin. Canada primarily allocates citizenship through jus soli, meaning that every child born on ‘Canadian soil’ automatically obtains citizenship, regardless of the citizenship or residency status of the parents. The two questions driving this research are: How has so-called ‘birth tourism’ been constructed in Canadian print news media and in federal legislative discussions? What do these discourses tell us about who is deemed ‘deserving’ and who is deemed ‘undeserving’ of Canadian citizenship? The answers to these questions are derived from two sources: a critical discourse analysis of parliamentary Hansard and committee meetings, and a discourse and content analysis of 80 French and English language Canadian newsprint articles on the topic from 1990 to 2021. To date, political and news media discourses have largely framed this alleged practice as a ‘problem’ to be ‘solved’. The analysis reveals that, beyond relying on racial and gendered stereotypes of women of colour, particularly Chinese women, newsprint media and political discourses have largely employed a dichotomy popularized by the Conservative party between ‘good’ immigrants and ‘bad’ immigrants, with so-called ‘birth tourists’ falling in the latter category. The examination of these discourses reveals underlying assumptions about who is considered ‘undeserving’ of citizenship. Based on these assumptions, children born in Canada to non-resident mothers are not considered deserving of Canadian citizenship because their mothers subverted state-sanctioned immigration and citizenship pathways and failed to properly participate in the white settler nation-building that is typically conditional to acquiring citizenship.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".