Exploring the linguistic signature of interpersonal liking in second language interaction
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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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 | 0.001 | 0.009 |
| 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".