Opinion polarization and its connected disagreement: Modeling and modulation
Bibliographic record
Abstract
Divergent opinions resulting from polarization are widespread across various fields, including economics, technology, and politics, and are often considered the genesis of disagreement among people. Numerous studies were therefore devoted to achieving complete consensus. However, as we show here, polarization is inevitable when individuals exhibit the self-confidence effect in interpreting social pressure, a psychological mechanism that drives adaptive consolidation of biased opinions. We also demonstrate that polarization, which merely reflects opinion distribution, does not necessarily cause high-level connected disagreement, which is highly related to the random walk normalized Laplacian (RWNL) of a network. By developing a networked dynamical model incorporating the self-confidence effect and analyzing the boundaries of opinion patterns, we find that polarization and its connected disagreement have different formation mechanisms. The level of connected disagreement intensifies as the number of unstable eigenmodes of the RWNL increases, a process greatly influenced by network topology. This finding helps us elucidate how connected disagreement evolves across different social networks and, more importantly, provides insights into developing effective modulation strategies to mitigate the level of connected disagreement when eliminating polarization is difficult.
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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 | 0.000 | 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) | 0.000 | 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".