I Don’t Think You like Me: Examining Metaperceptions of Interpersonal Liking in Second Language Academic Interaction
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it