Social Theory and Everyday Marxists: Russian Perspectives on Epistemology and Ethics
Bibliographic record
Abstract
Abstract Scholars have long tracked how the USSR, a laboratory of social engineering, was deeply informed by local readings of Marxist social theory. Why, then, in recent years, have so many historical and anthropological studies of Russia excluded “Marxist” from the list of main descriptors, or optics, through which they view their material? In this essay, I argue that in much contemporary scholarship Marxism and its many afterlives have evidenced a kind of blind spot, reducing Marxism to “just” an ideology. I assert that rediscovering the presence of Marxism in Russia as a Gramscian hegemonic process and a vernacular that emerged among “laymen” can help us understand how a wide range of Russians continue to make sense of their worlds today. Drawing on several years of research in the city of Perm, I interpret everyday conversations among middle-age urbanites about morality, and demonstrate how this rediscovery of Marxism can elucidate what things matter for Russians today, and how. If social scientists proceed by acknowledging that “professional” and “lay” social knowledge increasingly share sources of “theoretical” inspiration, then we face a range of narrative challenges.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.063 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".