Bibliographic record
Abstract
Over the past several decades, the demographic populations of many countries such as Canada as well as the United States have greatly transformed. Most striking is the influx of recent immigrant families into North America. As children lead the way for a 'new' North America, this group of children and youth is not a singular homogenous group but rather, a mosaic and diverse ethnic, racial, and cultural group. Thus, our current understanding of 'normative development' (covering social, psychological, cognitive, language, academic, and behavioral development), which has been generally based on middle-class Euro-American children, may not necessarily be 'optimal' development for all children. Researchers are widely recognizing that the theoretical frameworks and models of child development lack the sociocultural and ethnic sensitivities to the ways in which developmental processes operate in an ecological context. As researchers progress and develop promising forms of methodological innovation to further our understanding of immigrant children, little effort has been placed to collectively organize a group of scholarly work in a coherent manner. Some researchers who examine ethnic minority children tended to have ethnocentric notions of normative development. Thus, some ethnic minority groups are understood within a 'deficit model' with a limited scope of topics of interest. Moreover, few researchers have specifically investigated the acculturation process for children and the implications for cultural socialization of children by ethnic group. This book represents a group of leading scholars' cutting-edge research which will not only move our understanding forward but also to open up new possibilities for research, providing innovative methodologies in examining this complex and dynamic group. Immigrant Children: Change, Adaptation, and Cultural Transformation will also take the research lead in guiding our current knowledge of how development is influenced by a variety of sociocultural factors, placing future research in a better position to probe inherent principles of child development. In sum, this book will provide readers with a richer and more comprehensive approach of how researchers, social service providers, and social policymakers can examine children and immigration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".