Identity development of second-generation Chinese Canadians through heritage and dominant cultures
Bibliographic record
Abstract
This paper addresses the experiences of second-generation Chinese Canadians in developing their identity through heritage and dominant cultures. Literature on global migration and dual nation identities is explored before discussing the dichotomy of collectivism and individualism, which highlights a distinction between focusing on community goals compared to autonomy. The negotiation between heritage and dominant cultures is further impacted by a range of factors, including specific cultural values such as filial piety, as well as parental attitudes and components of ethnic identity (e.g. self-labels and language). Reviewing the existing, albeit limited, literature on Chinese Canadians has emphasized the shortcomings of data on this demographic and the lack of information on a population that has deep-rooted historical and unique connections with Canada. Amongst the studies available, the results have painted a picture riddled with a blend of generational statuses, a hyperfocus on adolescent individuals, and sampling mostly limited to metropolitan communities. Recognizing the complexities faced by second-generation Chinese Canadians, practitioners are required to acknowledge the unique experiences and local resources pertaining to this group of people. Further, self-awareness and cultural competencies strengthen the ability to align with prospective clients. Implications for the helping professionals and considerations for future studies are discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".