Sociological Dimensions of Justice: Analyzing Demographic Disparities in the Aftermath of the Capitol Insurrection
Bibliographic record
Abstract
Abstract Purpose – This chapter explores the socio-political and cultural factors contributing to the January 6th Capitol insurrection, with particular focus on the demographic profiles of the participants and the role of former President Donald Trump’s rhetoric. The research also investigates how social media platforms facilitated the dissemination of misinformation, analyzing these elements through the lens of Bourdieu’s concept of habitus. Methodology/approach – The study employs quantitative analysis of a data set comprising over 1,200 individuals charged in connection with the insurrection. Demographic and geographic data were analyzed to uncover patterns in participant profiles and legal outcomes. Findings – The findings indicate that the insurrectionists were primarily mid-life adults motivated by socio-economic grievances and driven by disinformation. Trump’s rhetoric played a crucial role in legitimizing their actions. Geographic analysis shows that conservative states contributed a disproportionate number of participants, with harsher sentencing outcomes observed in these regions. Additionally, gender dynamics in sentencing reveal that women were more likely to plead guilty and receive probation. Originality/value – This study offers a novel integration of Bourdieu’s habitus theory with demographic and geographic data to explain how socio-cultural factors, political rhetoric, and social media misinformation contributed to the January 6th insurrection. The research provides valuable insights into domestic extremism and suggests the need for regulating online platforms and holding political figures accountable for inciting violence.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".