Do they like me?
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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