Learning democracy: The political resocialization of immigrants from authoritarian regimes in Canada
Bibliographic record
Abstract
The results are unequivocal. First, immigrants from authoritarian regimes need to learn democracy. Compared to the local population, they are less likely to express opinions and more sceptical about citizen competence. Moreover, many do not believe that the government would listen to them if they were to speak out. Furthermore, immigrants who have little experience of democracy are more likely to have positive views about authoritarian regimes. At the same time, however, they are strong supporters of democracy and of Canada's political institutions. Second, immigrants' understanding of democracy is somewhat deficient upon arrival in Canada, but they successfully respond to the challenge of learning the norms of the host-political system. With the passage of time, their political outlooks slowly start to resemble that of people socialized in a democracy. And third, it seems that immigrants who successfully adapt to a democratic environment accomplish this transition by relying in part on their resources and political awareness. The vast majority of immigrants now arrive in Canada with little democratic experience; they have been socialized in authoritarian regimes. What is the impact of such a socialization under authoritarian regimes on immigrants' political beliefs? Do these newcomers learn democracy? And how do they learn democracy? This dissertation addresses these three questions. This dissertation has major implications for social science research. It provides crucial insights to immigration research in Canada and other Western democracies; it helps understand the dynamics of democratization; and sheds light on what it means to be a democratic citizen. Finally, this dissertation contributes to a rehabilitation of political socialization as a field of study.
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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 | 0.001 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".