Issues and challenges facing immigrant children as portrayed in children’s literature
Bibliographic record
Abstract
Canada is a country built on immigration and it continues to grow with the arrival of new immigrants from many lands. Children, who arrive as immigrants, experience many challenges that the citizens of the host country are unaware of or fail to recognize. In the analysis of informational accounts, the challenges faced by immigrant children fall into six major categories: language barriers and communication difficulties, maintaining ethnic culture, culture shock, intergenerational conflict, uprooting and separation issues, and prejudice and racism. These challenges having been determined, fictional accounts of the newcomers' experiences were analyzed to determine the authenticity of the portrayal of these challenges. It was found that characters experienced the isolation resulting from being unable to communicate in the host country's language. Loneliness due to uprooting was experienced by all the protagonists and the importance of memories and maintaining cultural traditions concerned most newcomers. Immigrant children in these narratives suffered from bullying and prejudice as well as conflicts within the family because of clashes between heritage culture and the culture of the host country. Canadian books do present these fictional accounts with empathy and realism; although the quantity of titles is small, the quality is certainly high!
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".