I Don’t Think You like Me: Examining Metaperceptions of Interpersonal Liking in Second Language Academic Interaction
Bibliographic record
Abstract
People often think about how they are perceived by others, but their perceptions (described as metaperceptions) are frequently off-target. Speakers communicating in their first language demonstrate a robust phenomenon, called the liking gap, where they consistently underestimate how much they are liked by their interlocutors. We extended this research to second language (L2) speakers to determine whether they demonstrate a similar negative bias and if it predicts willingness to engage in future interactions. We paired 76 English L2 university students with a previously unacquainted student to carry out a 10 min academic discussion task in English. After the conversation, students rated each other’s interpersonal liking, speaking skill, and interactional behavior, provided their metaperceptions for their partner’s ratings of the same dimensions, and assessed their willingness to engage in future interaction. We found a reliable interpersonal liking gap for all speakers, along with speaking skill and interaction behavior gaps for female speakers only. Only the female speakers (irrespective of their partner’s gender) seemed to factor metaperceptions into their willingness to engage in future communication. We discuss the implications of these initial findings and call for further work into the role of metaperception in L2 communication.
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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 | 0.011 |
| 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".