Identity Development of Heritage Language Learners in the Study Abroad Context: A Literature Review from the Chinese Heritage Language Perspective
Bibliographic record
Abstract
Study abroad (SA) programs often underrepresent minority students, including heritage language learners, and overlook their unique needs. This study provides a critical literature review on the identity development of Chinese heritage language learners (CHLLs) in the SA context, a significant yet underexplored topic compared with more commonly studied heritage languages, such as Spanish and French in the field. While identity plays a crucial role in SA experiences, studies on CHLLs remain limited and are often constrained by small sample sizes, limited program types, and overgeneralized CHLL group categorizations. Given the distinct challenges of CHLLs, including linguistic diversity and sociopolitical factors, this review underscores the need for more comprehensive and controlled research. A deeper understanding of CHLLs' experiences will offer insights into the complexity of identity negotiation in heritage language learning and contribute to more inclusive SA program development. Abstract in Mandarin Chinese 出国留学（SA）项目往往低估了少数群体学生的参与，包括语言传承学习者，并忽视了他们的独特需求。本研究提供了一项关于中国语言传承学习者（CHLLs）在留学背景下身份发展的关键文献综述，这一话题在该领域中相较于西班牙语和法语等较为常见的语言传承研究，仍然是一个重要但被忽视的领域。尽管身份在留学体验中扮演着至关重要的角色，针对CHLLs的研究仍然有限，且通常受限于样本量小、项目类型单一和对CHLL群体类别的过度概括。鉴于CHLLs面临的独特挑战，包括语言多样性和社会政治因素，本综述强调了进行更为全面和受控研究的必要性。深入理解CHLLs的经验将为语言传承学习中的身份谈判复杂性提供见解，并有助于推动更具包容性的留学项目发展。
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".