The Information Used to Judge Supportiveness Depends on Whether the Judgment Reflects the Personality of Perceivers, the Objective Characteristics of Targets, or their Unique Relationships
Bibliographic record
Abstract
People who judge their relationships as more supportive enjoy better mental health than people who judge their relationships more negatively. We investigated how people made these judgments; specifically, how people weighed different types of information about targets under three different conditions: when judgments reflected the personality of perceivers, the objective characteristics of targets, and the unique relationships between perceivers and targets. Participants (i.e., perceivers) judged the same four videotaped targets on personality, similarity to perceivers and likely supportiveness. As in previous research, perceivers based their judgments on perceived target similarity to perceivers, and on target personality. However, how perceivers weighed personality and similarity information varied dramatically depending upon whether the judgment reflected the personality of perceivers, the objective characteristics of targets, or the relationship between perceivers and targets. Implications for understanding how people make support judgments were discussed.
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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 | 0.006 | 0.060 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".