Social Equality: Cognitive Modeling Based on Emotional Coherence Explains Attitude Change
Bibliographic record
Abstract
Why do people have conflicting views of equality concerning the distribution of income, wealth, and satisfaction of vital needs? How do people form and sometimes change their views of equality and related issues, such as gender identity? Answers to such questions can benefit from cognitive science—the interdisciplinary field that includes neuroscience and computer modeling as well as psychology. According to principles of emotional coherence, attitudes develop and change because of connections among the values attached to systems of concepts, beliefs, and goals. People attach a positive value to concepts such as equality, if the concept fits with other positive concepts such as human needs, and opposes negative concepts such as poverty. Emotional coherence balances positive and negative values to yield an overall conclusion. Computer models based on emotional coherence explain people’s differing attitudes about equality and issues such as transgender rights. They also model how people sometimes change their minds.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".