Interpersonal liking, cultural belonging, and heritage language: exploring the role of metaperception in interaction between heritage speakers of Vietnamese
Bibliographic record
Abstract
Metaperceptions (or the impressions people believe to make on others) are a potential source of misconception in conversations involving members of the same ethnic community. We investigated whether speakers’ tendency to underestimate how they are perceived by others has consequences for future interaction. In this quantitative study, we paired 46 previously unacquainted speakers of Vietnamese as a heritage language (23 second-generation speakers, 23 recent immigrants) for two conversations. The speakers in each pair, recruited through convenience sampling, were similar in age (all young adults, with a range of 18–39 years). Each pair included one second-generation speaker born in Canada, age-matched with one immigrant, with a balanced distribution of speakers’ gender (eight pairs of women, seven pairs of men, and eight mixed pairs). After each conversation, the speakers used a 100-point scale to assess each other’s interpersonal liking, cultural belonging, and heritage language ability, provided their metaperceptions for their partner’s ratings, and assessed their willingness to engage in future interaction. Results of statistical comparisons (ANOVAs, correlations) indicated that all speakers underestimated how their partner perceived their interpersonal liking and heritage language (but not cultural belonging), but all ratings improved over time. However, only Vietnam-born speakers seemed to factor their perceived cultural belonging into their willingness to engage in future communication. We discuss implications of these findings for intragroup cohesion and contact.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".