Words tell the Tale: the Motivation behind A. N. Afanas’ev’s Editing of Sources From the Archives of the Russian Geographical Society for his Narodnye russkie skazki
Bibliographic record
Abstract
This article is based upon a comparison of source documents, originally drafted by local scribes and submitted to the Russian Geographical Society, with the final, edited, versions of these stories in Afanas’ev’s famous collection. It discusses how Afanas’ev’s editorial work reflected his ideals of Russian language and culture, which were shaped by the intellec- tual values of his time. Afanas’ev ‘nationalized’ the tales by replacing some words of foreign origin with words of Russian origin. He also eliminated Church Slavonic vocabulary in an attempt to popularize and ‘Russify’ the texts, while at the same time adding some regional words to ensure an authentic tone. Paradoxically, Afanas’ev replaced some regionalisms with their Muscovite forms. The descriptions of violence were tempered to enhance the suitability of the material for children. The steps taken conspire to suggest an editorial method designed to promote a modern Russian language and a concept of history and culture.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".