From immigrants to ideal citizens: Canadian government approaches to molding newcomers
Bibliographic record
Abstract
The construction of ideals of citizenship, in its social, political and cultural aspects, is a vital part of collective identity formation in contemporary states. Western democratic countries, such as Canada, that have historically endorsed immigration, have today developed a substantial state-sponsored political culture which is transmitted to newcomers through citizenship education programs. Considering how immigration has been and continues to be central to the history and the evolution of Canada, it becomes pertinent to analyze the question of how national identity and citizenship are cultivated and shaped through Canadian immigration policies and programs. What procedures does the Canadian government undertake to mold immigrants into Canadian citizens? What is the main citizenship discourse adopted by the Canadian government, and how is this discourse manifested/reflected in particular interventions related to the acquisition of citizenship by newcomers to Canada? This thesis seeks to shed light on the procedures undertaken by the Canadian government to shape and create Canadian citizens as an outcome of the immigration process. In order to answer these questions, three different policies/programs developed by the Canadian government will be explored, being: (1) the Canadian Orientation Abroad (COA) pre-arrival program (2) citizenship tests/ interviews and (3) Oath of citizenship ceremonies. The discourse analysis and semi-structured interviews conducted for this project suggest that Canadian citizenship discourse in the national and international spheres are part of a larger set of policies that are aimed at socializing newcomers into a neoliberal model of citizenship, in which “ideal” citizens are productive for the Canadian economy. Indeed, Canadian citizenship discourse is strongly focused on the economic integration of newcomers (the neoliberal citizen) as opposed to their social integration. The obligations and duties of individuals are central elements in Canadian citizenship discourse, which can create feelings of exclusion among newcomers. Despite endorsing multiculturalism in the three policies/programs examined in this thesis, the Canadian government presents a uniform image of Canadian citizenship without explicitly recognizing cultural rights and existing power dynamics in its society
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.059 | 0.048 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".