Trust Me, He’s Not Right for You: Factors Predicting Trust in Network Members’ Disapproval of a Romantic Relationship
Why this work is in the frame
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Bibliographic record
Abstract
When individuals experience disapproval of their romantic relationship from friends or family members, how do they determine whether they should trust or believe that negative opinion? In this study, we examined a hypothesised model in which level of perceived relationship expertise, level of perceived bias, quality of evidence provided, and level of perceived approval for the romantic relationship from the broader social network predicted levels of trust/distrust in a disapproving opinion. Using hierarchical multiple regression, we found support for the hypothesised model in an online study (N = 173). Contrary to expectations, no differences in the model were found by relationship type (marginalised vs. non-marginalised). Implications and theoretical explanations for the findings are discussed.
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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 | 0.001 | 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