Exploring the linguistic signature of interpersonal liking in second language interaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People worry about how they are seen by others, but their insights (called metaperceptions) are often too negative. For instance, many speakers believe that their interlocutors like them less than they actually do, and these overly negative metaperceptions inform speakers' actions such as asking for advice or pursuing friendships. Our goal was to understand if low, underconfident metaperceptions are associated with specific interactional behaviors for second language (L2) speakers, as a way of identifying a “linguistic signature” of insecure metaperceivers. We analyzed 10-min dyadic conversations by 37 L2-speaking university students discussing academic texts. Following the conversation, students provided their metaperceptions (how much they thought their partner liked them) and their actual assessments (how much they liked each other). We coded the conversations for eight measures of utterance fluency (repetitions, repairs, filled pauses, discourse markers) and speaker engagement (lexical content, mean length of turn, backchannels, overlapping speech). Whereas several measures predicted students' metaperceptions, none contributed to their actual assessments. Speakers who felt appreciated by their partner provided more lexical content across shorter conversational turns, whereas those who felt insecure assumed a dominant role speaking in long turns. These findings provide initial insights into how speakers’ metaperceptions manifest in their interactional behavior. • Speakers tend to underestimate their liking by conversation partners. • English L2 speakers' conversations were coded for fluency and engagement behaviors. • Speakers also provided perceived and actual ratings of each other's liking. • Speakers with higher perceived ratings provided more content across shorter turns. • No linguistic measure predicted speakers' actual liking by conversation partners.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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