Examining Acculturation of Immigrant Children in the Domain of School and Education: Understanding the Mechanism of Acculturation Using the Theory of Sociocultural Models
Bibliographic record
Abstract
This project used the theory of sociocultural models (TSCM; Chirkov, 2020a; 2020b) to examine the acculturation mechanism used by Indian immigrant children to navigate the Canadian educational context. According to the TSCM, acculturation involves navigating the discrepancy between the immigrants’ home and host cultures’ sociocultural model (SCM) of education. A system of SCMs is a primary mechanism for regulating people’s activities in every domain of their lives in different cultures. It is the rule that the SCM for education in their home country differs from the model in their host country, and immigrants and their children must discover these differences and navigate them. Following this logic, the initial stages of the project involved extracting a student-centered Canadian and a teacher-centered Indian SCM of education. First, they were identified using a comprehensive literature review and then examined through qualitative interviews with Canadian educators and Indian immigrant caregivers. The cultural discrepancies between the Canadian and Indian public education models were identified. In the second phase, case studies of immigrant caregivers illuminated the parents’ interactions with their children and with schools. Finally, immigrant children’s navigation between these SCMs and their families’ demands was studied using interviews. The results identified the children’s adjustment to the education domain, uncovering an acculturation mechanism involving the newcomer child at the center of a triangle of school, home and peer contexts. They reflected upon all three contexts to navigate their demands, interpreting and evaluating their place to find a harmonious balance for a successful education and happy lives.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".