In the eye of the beholder: national boundary perceptions and their identity implications across immigrant generations in multinational states
Bibliographic record
Abstract
The concept of national boundaries has been employed extensively in the social sciences, with most research focusing on the host society's perspective. This study innovates by measuring immigrants’ perceptions of how boundaries of their host nation are drawn and examining how such perceptions impact their national identifications in a multinational context, more specifically in the province of Quebec in Canada. It relies on a stratified sample of first- (n = 1129) and second-generation immigrants (n = 1286) as well as non-immigrants (n = 1472). We show that boundary perceptions impact the availability and attractiveness of different identity options, including identification with Quebec, the country of origin, and Canada. First, while perceptions of ascriptive boundaries to Quebec push first- and second-generation immigrants away from Quebec identity, the reactive effect of strengthening identification with the country of origin and Canada is limited. Second, although the second generation perceives Quebec boundaries as more ascriptive than the first generation, it is not more strongly impacted by their boundary perceptions in their identifications. Third, perceptions of attainable boundaries promote all three national identifications, but only for the first generation. Finally, we demonstrate that attention to boundary perceptions adds important explanatory leverage beyond the impact that discrimination has on national identifications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".