Bibliographic record
Abstract
The main question driving this study is: How are country-of-origin characteristics related to immigrants’ political integration? The response to that question begins with an evaluation of what it means to integrate politically. Conventional wisdom suggests that political integration is conceptualized on a single dimension. However, this thesis argues that political integration is a multi-dimensional concept, and both behavioural and affective dimensions are important. Exploring a multi-dimensional conceptualization is more challenging from an empirical point of view. The core of the following analyses leverages both individual-level microdata and aggregate country-level statistics. The individual-level data consist of over 8,000 immigrant observations in Cycle 27 (2013) of the General Social Survey (GSS), providing a more reliable representation of immigrants from various source countries. The aggregate-level data, sourced from the Quality of Government (QoG) dataset, Polity IV, and the World Values Surveys (WVS), are instrumental in critically examining the assumptions of previous research. The study identifies which country of origin factors, including democratic status, civic engagement, colonial history, globalization, and value orientations, impact different aspects of political integration. It finds that the democratic status of the country of origin is important for some dimensions of political integration, confirming findings from previous research (Bilodeau, McAllister, and Kanji 2010; Finifter and Finifter 1989; Jones-Correa 2016; Wals 2008, 2011; White et al. 2008), but not others. For example, the democratic status of a country of origin is related to immigrants’ participation in both formal and informal political activities in Canada, but not to their civic engagement. This study demonstrates that the degree of civic engagement in an immigrant’s country of origin is a significant determinant of participation in civic organizations in Canada. Moreover, a country of origin’s colonial history and its globalization levels can shape immigrants’ development of affective national belonging in Canada.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".