Do they like me?
Bibliographic record
Abstract
Abstract People are frequently concerned about the impressions they make on others (referred to as metaperceptions), but their insights are often inaccurate. Illustrating the phenomenon called the liking gap, speakers interacting in their first language (L1) and second language (L2) tend to underestimate how much they are liked by their interlocutor, and these judgments often predict their desire to engage in future interaction and collaboration. To understand the scope of this bias and its consequences, we focused on L1–L2 dyadic interaction, examining metaperception as a potential barrier to conversations between university students. We recruited 58 previously unacquainted university students to perform a 10-min academic discussion task between one L1 and one L2 speaker. Afterward, the speakers (a) assessed each other’s interpersonal liking, speaking skill, and interactional behavior; (b) provided their metaperceptions of their interlocutor’s assessments of the same dimensions; and (c) estimated their interest in future interaction with the same interlocutor. All speakers showed a reliable metaperception bias to underestimate their interpersonal liking, speaking skill, and interactional behavior. However, only L1 speakers’ desire to engage in future interaction was associated with their metaperceptions of interpersonal liking. We discuss implications of this finding for understanding and promoting academic 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".