Actual vs. Perceived Liking Gaps: How Adjustment Relates to Liking Judgments in First Impressions
Bibliographic record
Abstract
People’s beliefs about how much they are generally liked (i.e., meta-liking judgments) are less positive than liking judgments, a finding termed the “liking gap”. We build on this past literature by distinguishing between an actual liking gap (i.e., the experience of believing one is less liked by others than one actually is) and a perceived liking gap (i.e., the experience of believing one likes others more than how much others like them). Using a platonic and romantic first-impression sample, we examined the links between adjustment and these liking gaps. Overall, people displayed both liking gaps. Although there was some evidence that more adjusted people were less likely to display a perceived liking gap, adjustment was not related to displaying an actual liking gap. In fact, adjustment was simply related to holding more positive meta-liking judgments. Thus, the present work contributes to the advancement of the social perception and judgment literatures.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".