A Constant Error, Revisited: A New Explanation of the Halo Effect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Judgments of character traits tend to be overcorrelated, a bias known as the halo effect . We conducted two studies to test an explanation of the effect based on shared lexical context and connotation. Study 1 tested whether the context similarity of trait names could explain 39 participants’ ratings of the probability that two traits would co‐occur. Over 126 trait pairs, cosine similarity between the word2vec vectors of the two words was a reliable predictor of the human judgments of trait co‐occurrence probability (cross‐validated r 2 = .19, p < .001). Two measures related to word similarity increased the variation accounted for in the human judgments to 45%, cross‐validated ( p < .001). In Experiment 2, 40 different participants judged similarity of word meaning within the pairs, confirming that the word pairs were not simply synonymous (Average [SD] = 40.8/100 [13.1/100]). Shared lexical context and word connotation play a role in shaping the halo effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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