Economic immigrant parents’ educational involvement in Prince Edward Island
Bibliographic record
Abstract
As a strategy for addressing the issues of aging population and economic development, the\nCanadian government collaborated with provinces and territories and continued to develop\npolicies and programs to recruit substantial economic immigrants in recent decades. Many\neconomic immigrants choose Canada as a destination country because of its quality education\nsystems and services for their children, and they also eagerly search opportunities to engage in\ntheir children’s schooling after landing in Canada. The purpose of this study was to investigate\neconomic immigrants’ parental involvement in their children’s education in Prince Edward Island\n(PEI), Canada. Specifically, this research examined (1) immigrant’s obligations and expectations\nwhile involving themselves in children’s education; (2) immigrants’ parental involvement in\nchildren’s education; (3) the essential challenges and issues influencing immigrants’ parental\ninvolvement; and, (4) immigrants’ perception for their educational involvement in public schools.\nAnswers to these topics are critical for schools, educators, and parents as they will eventually\ninform the best policy, program, and practices supporting immigrants’ parental involvement in\neducation.\nInformed by Epstein's (2001) Parental Involvement Framework, McNeal (1999) and Coleman's (1988) Social Capital Theory, this research study adopted a qualitative research design to\nunderstand economic immigrants’ experiences and perception of their parental involvement in\nCanadian public schools. Eight immigrant parents from five different countries voluntarily\nparticipated in the study. Data analysis revealed four major sources for immigrants to collect\nneeded information, eight challenges and five requests calling to inform their parental\ninvolvement in education. The immigrants not only hold high expectations for their children, but\nalso actively involve themselves in home-based learning and parent-teacher communications. However, these parents scarcely had opportunities entering the role of leadership and not engaging in community collaboration mainly due to language barriers and culture differences.\nRecommendations generated from the findings include offering immigrant parents workshops on\nCanadian education and cultures, and fostering cross-cultural sensitiveness for family-school communication through professional development for educators.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".