Rights Framing and Perspectives on Immigration Canada
Bibliographic record
Abstract
This study explores whether appeals to human rights or Canadian values are effective in generating support for the extension of social protections to racialized and/or non-citizen residents in Canada. The project builds on previous research in the US (Voss et al. 2020) that examined whether framing hardships such as food insecurity and lack of access to health care as violations of civil rights, human rights, or American values affected public attitudes regarding rights or value violations or support for government action to address such hardships. The California-based experiment found that rights-based language had little effect on support for government action and that registered voters made clear distinctions regarding noncitizens’ deservingness for government assistance on the basis of their legal status. In this experiment, we similarly test whether human rights or national (“Canadian”) values frames have an impact on support for protections in Canada, and whether these differ on the basis of the individual’s racial or legal status in Canada. Principally, we analyze Canadians’ response to two rights-violation scenarios – food insecurity and police targeting – to assess if rights-based or value-based appeals affect Canadians’ support for co-citizens and noncitizens. Further, we manipulate the race and legal status of the individual profiled in the vignette to test whether such factors impact participants’ reactions to the frames. We anticipate that the frames will exert some effects on public attitudes regarding rights or values violations and support for government action, but that these effects will differ on the basis of race and legal status.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".