Ethnic Minority Parents' Involvement in Education: Exploring and Understanding Their Experiences
Bibliographic record
Abstract
Lopez, Scribner and Mahitivanichcha (2001) discuss the limited volume of \nliterature that directly addresses ethnic minority parents' involvement in their children's \neducation and they call upon researchers to fill this gap in the literature. This study is one \nsuch positive step with its focus on exploring how ethnic minority parents of secondary \nschool students in southern Ontario understand their involvement in their children's \neducation. Participants in the study included three ethnic minority parents recruited from \na local adult education centre, and my parents who, as ethnics minority parents, also \nfaced challenges trying to support their children as we progressed through the Ontario \neducational system. Primary data were collected through in-depth, open-ended interviews \napproximately one hour in length. Each of the five participants was interviewed twice. \nSecondary data included Ontario Ministry of Education documents that addressed \nprograms, policies, and supports for ethnic minority students in Ontario secondary \nschools. Fieldnotes and a research journal also provided secondary data. \nThe findings highlight, among other things, the challenges the participants faced \nas ethnic minority parents with a deep desire to support their children's education, but \noften lacking the cultural capital valued in the Ontario school system to meet that goal. \nAs well, I benefited greatly from this research learning about the various ways in which, \nin my future work as a teacher of ethnic minority students, I can integrate the knowledge, \nskills, and experiences of ethnic minorities into my practice to ensure that parents of the \nnon-dominant culture have an opportunity to become highly involved in the education of \ntheir children.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".