The Shame System Operates With High Precision
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
Previous research indicates that the anticipatory shame an individual feels at the prospect of taking a disgraceful action closely tracks the degree to which local audiences, and even foreign audiences, devalue those individuals who take that action. This supports the proposition that the shame system (a) defends the individual against the threat of being devalued, and (b) balances the competing demands of operating effectively yet efficiently. The stimuli events used in previous research were highly variable in their perceived disgracefulness, ranging in rated shame and audience devaluation from low (e.g., missing the target in a throwing game) to high (e.g., being discovered cheating on one's spouse). But how precise is the tracking of audience devaluation by the shame system? Would shame track devaluation for events that are similarly low (or high) in disgracefulness? To answer this question, we conducted a study with participants from the United States and India. Participants were assigned, between-subjects, to one of two conditions: shame or audience devaluation. Within-subjects, participants rated three low-variation sets of 25 scenarios each, adapted from Mu, Kitayama, Han, & Gelfand (2015), which convey (a) appropriateness (e.g., yelling at a rock concert), (b) mild disgracefulness (e.g., yelling on the metro), and (c) disgracefulness (e.g., yelling in the library), all presented un-blocked, in random order. Consistent with previous research, shame tracked audience devaluation across the high-variation superset of 75 scenarios, both within and between cultures. Critically, shame tracked devaluation also within each of the three sets. The shame system operates with high precision.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.007 |
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