Служба по выборам: портрет уездной элиты российского дворянства последней четверти XVIII в.
Bibliographic record
Abstract
In the article is investigating the problem of ineffective work of the nobility organs of self-government in Russian empire. On the base of literary and historical sources the author reconstructed «service portraits» of uyezd nobility of the last quarter of XVIII century, which were electing for various posts. In the article also is determined professional incompetent of the offi cials as a factor, which didn’t allow to the nobility organs of self-government to be an effective part of the whole structure of imperious institutions in Russian empire. = Исследуется проблема причин неэффективной деятельности дворянских органов самоуправления в Российской империи. При использовании исторических и литературных источников автором как бы воссозданы «служебные \nпортреты» уездного дворянства последней четверти XVIII в., избиравшегося на различные должности. Профессиональная некомпетентность должностных лиц, отсутствие у большинства из них желания заниматься «черновой» \nуправленческой деятельностью были определены автором как факторы, не позволившие дворянским корпоративным органам стать эффективно действующей частью всей структуры властных институтов Российской империи.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".