NARRATIVES IN AMERICA: THE CONNECTION BETWEEN AFFECTIVE POLARIZATION AND VICTIMHOOD IN THE 2020 US ELECTION
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it