Great Divisions: The Evolution of Polarization During the Man-made Emergency of January 6, 2021.
Bibliographic record
Abstract
Polarization, which refers to the formation of two opposing groups based on the users' beliefs and opinions, has a growing body of literature. However, social media polarization differs from offline polarization in that beliefs change almost instantaneously on social media as a result of events unfolding. We investigate the uses of social media communication that has resulted in polarized opinions among individuals prior to, during, and after the January 6th Capitol riots. Analyses of the dominant narratives on Twitter surrounding the incident reveal a high level of polarization throughout the unfolding of the event, with increased polarization possibly attributable to the onset of the crisis. We also observed that polarization is a dynamic phenomenon: as an event unfolds, polarization changes, and knowing how it changes is important for timely crisis resolution. We propose three measures of polarization that could be used to examine polarization accurately during a crisis.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.007 | 0.001 |
| 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".