Integration into the Canadian Society: Immigration, Language, and Sense of Belonging
Bibliographic record
Abstract
Canada’s significant immigrant population (21.9% according to Statistics Canada) has made the country multicultural and diverse, but it has also created unique challenges. For immigrants, becoming part of the Canadian society implies the negotiation of their place in the world, their identities and sense of belonging while struggling to breach the limitations imposed by a new language, culture and ways of thinking (Block 2014). Giving the significant population of immigrants living in Canada and in Calgary (36% foreign born residents), there is a need to study the barriers encountered by newcomers and the extent to which they obstruct intercultural practices and newcomers’ integration into their new home. My research focuses on learning (1) how languages influence the establishment of immigrants’ relations of belonging to the Canadian society, (2) how immigrants’ feelings of belonging or no-belonging are affected by different contexts – mental models that control how discourse is processed guaranteeing its appropriateness in a given situation, (3) the extent to which immigrants’ feelings of belonging or nobelonging are influenced by the lack of understanding of the frames that allow communication and (4) how contexts issues can be overcome to allow the construction of immigrants’ sense of belonging. In order to answer these questions, I will be conducting interviews, focus groups and digital storytelling workshops. This investigation will create a space to discuss the immigrant experience, promote diversity awareness and inform policy and practice in different fields to facilitate newcomers’ integration to the city of Calgary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.041 | 0.017 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".