Bibliographic record
Abstract
The legend that Karl Marx either said almost nothing on nationalism, race, and ethnicity, or that on these issues he was woefully mistaken and reductionist, has been maintained in axiomatic fashion for decades, despite numerous scholarly refutations. Of course, not everything Marx wrote on nationalism, ethnicity, and race holds up well today. One prominent example concerned the Russians and some of the other Slavic peoples of Eastern and Southern Europe. As will be discussed, Marx strongly supported Polish national emancipation as an important progressive force in European politics. But, in his early writings, he portrayed Russia as an utterly reactionary society, and described most of the other Slavic peoples as dominated by Russian Pan-Slavist propaganda. This has led to extended – and sometimes unfair – attacks on Marx on nationalism tout court . To a great extent, Marx’s views were connected to Russia’s counter-revolutionary role during the democratic revolutionary wave of 1848–9, but this is not a full explanation. For one can find in Marx’s writings on Russia before the 1870s not only violent denunciations of the Tsarist Empire as a malevolent force, but also a number of very problematic, even racist statements about the Russian people themselves. As to other Slavic groups, most of the vitriol was expressed by Friedrich Engels (1820–95) in a series of articles on Pan-Slavism. 2 Both Marx and Engels shifted their positions by the 1870s and the 1880s, however, when one can find Marx – who had learned Russian by this time – extolling the Russian peasant commune as a possible starting point for a global insurrection against the capitalist system. 3
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".