Teaching and/or learning Chinese as an additional language: Challenging terminology and proposed solutions
Bibliographic record
Abstract
The Chinese language is becoming one of the most important languages in the world. The demands and interests in learning Chinese as an additional language are rapidly growing, but research in this area has not kept up with the accelerated development of this promising field. A key issue immediately faced in this area of research is the variety of identifying or descriptive terms (e.g., Chinese as a second, foreign, international, heritage, subsequent, additional language) that are inconsistently used. As will be expanded on below, this has created confusion and difficulty in research and teaching and learning practice. This paper details the examination of these perplexing terms, and the proposal of teaching and/or learning Chinese as an additional language as a viable term, as it is politically and pedagogically appropriate. The purpose of the study is to identify terminology that can be used to deepen our understanding and enhance the quality of teaching and/or learning Chinese as an additional language in the present global culture.
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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".