The warrior muzhik and fakelore maiden: Russian banal nationalism on and offline
Bibliographic record
Abstract
The Warrior muzhik and fakelore maiden: Russian banal nationalism on and offlineA B S T R A C T. Nationalism is often understood in terms of grand political movements, political speeches and too often in wars pitting states against each other or nationalist insurgents rising against.Yet, nation and nationalism can be studied in the banal events of daily life as Michael Billig (1995) proposed.The flying of flags in front of houses, the draping of St. George ribbons or icons off of rearview mirrors, the small symbolic markings of nationhood -all these reinforce the nationalism that can be harnessed by larger political movements.This article will examine the banal in the cyberspace, notably how idealized images of masculinity and femininity are created, liked and shared on social media, and how such mundane daily affirmations of nationhood reinforce larger national narratives.Individuals are thus not passive recipients of national discourses, but can be active contributors to them by taking and sharing photos of themselves in folkloric dresses or working out in gyms.They can thus either reinforce or challenge the prevailing narratives and participate in the making of nations.This is clearly seen in Russian social media sites where online nationalism both buttresses and occasionally challenges older ideals of nation and gender, both intertwined in defining what it means to be Russian.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".