КОНСЕРВАТИЗМ, ЛИБЕРАЛИЗМ И КРЕСТЬЯНСКИЙ ВОПРОС В ОБЩЕСТВЕННО-ПОЛИТИЧЕСКОЙ МЫСЛИ РОССИИ НА РУБЕЖЕ XVIII-XIX ВЕКОВ
Bibliographic record
Abstract
The article considers the problem of the attitude of different directions of noble public opinion in Russia, namely liberals and conservatives, to peasant issue at the end of the XVIII the first quarter of the XIX century. The author of the work substantiates the thesis about the similarity of their approaches to this problem associated primarily with the desire of the landed nobility to retain the array of land ownership, as well as with the wish to continue their influence on the peasantry and some stability in the Russian society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.008 |
| Meta-epidemiology (narrow) | 0.013 | 0.014 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.019 | 0.007 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.019 | 0.048 |
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; both teacher heads agree on what is shown here.
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".