Learning Chinese as the United Nations Language? Implications for Language Learning Motivation and Identity in Adult Higher Education
Bibliographic record
Abstract
In this study, the researcher strives to build on earlier work in which the roles of motivation and identity in language learning in adult higher education were examined. In the present connection, the focus falls on the roles played by a seldom researched group consisting of staff members in an intergovernmental organization. This group is comprised of United Nations staff members (N=33/18 female, 15 male) involved with the United Nations Chinese language program at the UN Headquarter for the Asia region in Bangkok. The past research has shown that an adult language learner’s learning level becomes very high when s/he is sufficiently motivated. In this light, then, the researcher explores whether the language-learning process would be improved if adult staff members from an intergovernmental organization engage in language learning with a greater sense of a professional self/institutional identity than would be the case with a merely personal identity. Also considered is whether the language-learning process is enhanced by an individual/personal identity in contrast to having only a professional self/institutional identity. The findings show that UN staff members who are highly motivated to learn Chinese are more likely to harbor a mixture of both personal and professional identities. Nevertheless, prioritizing the learning of Chinese often stems from functional and practical reasons, i.e., from instrumental motivation. Finally, this study finds no clear links between Chinese heritage and success in Chinese language learning in the adult higher educational sector.
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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 | 0.003 | 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 | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".