Writing the 'Deep Story': A Comparative Case Study of the Rise of Nationalist Extremism in the United States and India
Bibliographic record
Abstract
This paper examines the global resurgence of far-right nationalist extremism, focusing on the rise of authoritarianism in the United States and India. Based on the theoretical framework of White Christian nationalism developed by Gorski and Perry (2022), which builds on Hochschild’s concept of ‘deep story’ (2016), the present analysis demonstrates how aspiring authoritarians strategically mobilize exclusionary ideologies to consolidate power. The ‘deep story’ of White Christian nationalism in the United States under Donald Trump can (1) provide valuable insight into the rise of Hindutva in Modi's India and (2) shed light on the transnational nature of contemporary far-right nationalist ideologies. Central to the analysis is the identification of the building blocks of the deep story: religiousness without religiosity as a marker of ethno-traditional group boundaries; the deification of political figures; nostalgia for a mythical past greatness; and the justification of violence and exclusion as necessary for national preservation. These building blocks construct a narrative of grievance and existential threat in which the dominant group perceives loss of status and projects anxieties onto foreigners and foreignness. Ultimately, the deep story of nationalist extremism functions to secure the consent of the dominant group for the acceptance of authoritarian rule.
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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 | 0.001 | 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 | 0.002 | 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) | 0.001 | 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".