NARRATIVES IN AMERICA: THE CONNECTION BETWEEN AFFECTIVE POLARIZATION AND VICTIMHOOD IN THE 2020 US ELECTION
Bibliographic record
Abstract
This study explores the emotions, beliefs, and deep stories about the self and other that are held by individuals on the political right and left in America in order to understand the manifestation of affective polarization during divisive historical moments. It also documents expressions of victimhood, villainhood, and privilege to determine how they intersect with narratives about the ingroup and outgroup. Horwitz (2018) argues that victimhood has become a desirable status in American politics and is thus a site of contestation. Therefore, we ask: what beliefs and emotions do individuals hold about the ingroup and outgroup and how do these contribute to exacerbating affective polarization? We conducted a four-month digital ethnography before, during and after the 2020 US election and developed an innovative approach to affective discourse analysis through an iterative, grounded study in order to analyse Facebook, Twitter, and Gab content. We coded 2500 cross-partisan posts/comments that focused on the January 6 Capitol events and election outcome/fraud and were underscored by themes of race and partisanship. Individuals on the political right and left expressed deep distrust towards the outgroup but thankfulness to those speaking their own narrative. Findings also indicate that affective polarization has deeper roots in feelings of bitterness and resentment of the other. These are linked to the ingroup’s narrative of victimhood/blame and serve to strengthen the boundaries of ingroup and outgroup identities as membership in the group becomes defined in part by the recognition (or lack thereof) of that group’s pain and oppression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".