Race, Citizenship/Immigration Status, and Contact with the Welfare State
Bibliographic record
Abstract
Abstract The ways in which welfare state programs structure people’s lives have been a central focus of research on policy feedback. While there is rich literature in the USA about racialized experiences with the state, we know little about how immigration history intersects with racial background in moderating experiences with the state nor have there been many studies in other liberal welfare regimes outside the USA. Our study aims to fill this gap by exploring how citizenship status over generations intersects with racial background in structuring interactions with welfare state programs in Canada. Analyzing data from Democracy Checkup surveys spanning from 2020 to 2023, we focus on how needs, capabilities, and experiences may structure government contact and the extent to which these factors explain differences across citizenship and racial categories. We document a recurring difference in the amount of contact among racialized respondents—non-citizens and third-generation citizens—that cannot be explained by either need or capability. Interestingly, our findings suggest that while the greater contact among racialized non-citizens is evaluated more positively in terms of procedure, third-generation racialized citizens generally evaluate their higher contact more poorly. These findings point to the importance of understanding racialized experiences with the state through the lens of 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".