The TAR Effect: When the Ones Who Dislike Become the Ones Who Are Disliked
Bibliographic record
Abstract
Four studies tested whether a source's evaluations of other individuals can recursively transfer to the source, such that people who like others acquire a positive valence, whereas people who dislike others acquire a negative valence (Transfer of Attitudes Recursively; TAR). Experiment 1 provides first evidence for TAR effects, showing recursive transfers of evaluations regardless of whether participants did or did not have prior knowledge about the (dis)liking source. Experiment 2 shows that previously but not subsequently acquired knowledge about targets that were (dis)liked by a source overrode TAR effects in a manner consistent with cognitive balance. Finally, Experiments 3 and 4 demonstrate that TAR effects are mediated by higher order propositional inferences (in contrast to lower order associative processes), in that TAR effects on implicit attitude measures were fully mediated by TAR effects on explicit attitude measures. Commonalities and differences between the TAR effect and previously established phenomena are 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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".