Integration into Canadian Society: Immigration, Language and Sense of Belonging
Bibliographic record
Abstract
Language allows individuals to place themselves in the world, telling others about who they are and allowing them to claim membership to multiple groups (Skinner et al., 2001, pp. 14-15). People engage in a never-ending process of claiming, rejecting, searching for and constructing an identity. In the context of immigration, identity negotiation is affected by language and structures of expectations that regulate how discourse is organized. This study examines narratives of immigration through the theories of sense of belonging and structures of expectation to understand the role of language in immigrants’ sense of belonging. The research focused on understanding to what extent language influences the establishment of immigrants’ relations of belonging to Canadian society and determines the ways in which immigrants’ feelings of belonging are affected by their structures of expectations. Data was collected through five digital storytelling workshops with 19 immigrants in the city of Calgary. NVivo (Qualitative Data Analysis Software) and Critical Narrative Analysis (CDA) were used to organize, code and analyze the data collected. The findings shed light on how language affects immigrants’ sense of belonging and how multiple frames, such as integration discourses and individuals’ experiences, affect everyday interaction. This study presents an argument against integration and in favor of developing a sense of belonging for immigrants based on the need for a joint effort of all members of the community to create a safe space in which differences and diversity are recognized, celebrated and encouraged.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.042 | 0.016 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".