Outside looking in: interpreting gossip appreciation in adults using an observer perspective
Bibliographic record
Abstract
My primary goal was to test two main theories of gossip: the social grooming theory and the cultural learning theory. Participants (N = 197) watched 30-second videos depicting four conditions where a gossip statement or a target present statement contained negative content that was: 1) relevant and violated a norm, 2) relevant and did not violate a norm, 3) irrelevant and violated a norm, or 4) irrelevant and did not violate a norm. Participants rated the speaker’s intent on the following dimensions: 1) sharing information, 2) strengthening relationships, 3) entertaining the listener, and 4) socially influencing the listener. They additionally rated the speaker’s attitude. Participants’ ratings indicated that statements that were relevant to the listener and contained a norm violation were better serving of gossip’s four functions than irrelevant statements without norm violations. However, this was generally the case for both gossip and target present statements with some caveats. Strengthening relationships ratings were increased in gossip conditions, albeit with a small effect size. However, when statements included a norm violation, gossiping increased entertaining and speaker attitude ratings. My findings indicate that both social grooming theory and cultural learning theory capture the main social function features of gossip, with the cultural learning theory having the largest impact Further, these features are impacted by gossip and target present scenarios by either strengthening or dampening the effects depending on the social function being measured.
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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 | 0.009 |
| 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 | 0.001 |
| 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".