Citizenship Revocation in the Mainstream Press: A Case of Re-ethnicization?
Bibliographic record
Abstract
Under the government of Stephen Harper’s Conservative Party (2006-2015), Canada witnessed numerous alterations of its immigration and citizenship rules. Under the new Citizenship Act (2014), dual citizens who have committed high treason, terrorism or espionage could lose their Canadian citizenship. In this paper, we examine how the measure was discussed in Canada’s mainstream newspapers. We ask: who/what is seen as the target of citizenship revocation? What does this tell us about the direction that Canadian citizenship is moving towards? As promoters of civic literacy, mainstream media disseminate information about government actions and legislation, interpret policies and are highly influential in forming public opinion. Our findings show that the newspapers were more often critical than supportive of the citizenship revocation provision. However, they also interpreted the measure as only likely to affect Canadian Muslims in general and omitted discussing the involvement of non-Muslim and, in particular, white, Western-origin Canadians in terrorist acts. Thus, despite advocating for equal citizenship in principle, Canadian Muslims were nonetheless constructed as less Canadian.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.045 | 0.042 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".