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РУССКО-КАБАРДИНСКИЕ ОТНОШЕНИЯ В 60-70-Е ГГ. XVIII В.: ИСТОРИОГРАФИЯ КОНФЛИКТА

2022· article· ru· W4405549024 on OpenAlexaboutno aff
Z.Zh. Glasheva

Bibliographic record

VenueVestnik Akademii nauk Čečenskoj Respubliki. · 2022
Typearticle
Languageru
FieldSocial Sciences
TopicSecurity, Politics, and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsHistoriographyEmpirePoliticsPeriod (music)Quarter (Canadian coin)HistoryAncient historyRussian historyPolitical scienceEconomic historyHumanitiesLawArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

Начиная с конца XVIII в. Россия кардинально меняет свою политику на Кавказе. Именно с этого периода она в основном сосредоточила свои усилия на расширении южных границ и постепенной интеграции горских народов в состав империи. Изменения в российской политической линии привели к неизбежному столкновению интересов России и Кабарды. Исследование показало, что к последней четверти XVIII в. в военный конфликт на Центральном Кавказе были втянуты не только кабардинцы, но и закубанские черкесы, ногайцы и чеченцы. В отечественной историографии события 1778-1779 гг. рассматриваются как поворотный момент в русско-кабардинских отношениях и, несомненно, представляют научный интерес. Since the end of the XVIII century. Russia is fundamentally changing its policy in the Caucasus. It was from this period that she mainly focused her efforts on expanding the southern borders and the gradual integration of the mountain peoples into the empire. Changes in the Russian political line led to an inevitable clash of interests between Russia and Kabarda. The study showed that by the last quarter of the XVIII century. not only the Kabardians, but also the Trans-Kuban Circassians, Nogais and Chechens were drawn into the military conflict in the Central Caucasus. In Russian historiography, the events of 1778-1779 are considered as a turning point in Russian-Kabardian relations and are undoubtedly of scientific interest. XVIII бI. Йуьххьехь дуьйна Россис буххера дуьйна хийцира шен Кавказера политика. ХIетахь дIадолийра къилбехьара дозанаш шордар, цо кхоьллира Россин а, ГIебартан а дуьхьдуьхьаллаттар. Талламе гойту цу конфликта йукъа гIербартойа, ногIий а, нохчий а озийна хилар. Исторнографино 1775-1779 ш.ш оьрсийн-гIебартойн йукъаметтигаш ирйалар гойту.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.004
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0160.007

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.

Opus teacher head0.045
GPT teacher head0.310
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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