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Record W4404774662 · doi:10.21267/aquilo.2020.9.9.020

АКТУАЛЬНЫЕ ПРОБЛЕМЫ ИСТОРИЧЕСКОЙ СОБЫТИЙНОСТИ

2020· article· ru· W4404774662 on OpenAlexfundno aff
ХВОСТОВА К.В.

Bibliographic record

VenueЦивилизация и варварство · 2020
Typearticle
Languageru
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
FundersLatvijas UniversitateDanmarks GrundforskningsfondSvenska Forskningsrådet FormasNational Research FoundationUniversiteit LeidenUniversity of CambridgeUniversity of CincinnatiMcGill UniversityPrinceton UniversityUniversity of OxfordHarvard UniversityDumbarton Oaks Research Library and CollectionAmerican Philological Association
KeywordsComputer science

Abstract

fetched live from OpenAlex

Автор анализирует понимание исторического события в рамках современной философии и исторической науки. Согласно философии М. Хайдеггера только крупномасштабные феномены, которые превосходят границы повседневности, могут рассматриваться как событие. Явления повседневности не являются событием. Принимая во внимание тесную связь крупных исторических событий и происшествий повседневности и на основе учета роли конкретики в рамках современной постнеклассической научной парадигмы, автор полагает, что социальные феномены повседневности должны в исторических исследованиях также рассматриваться как события. Анализируется роль математических методов в исторической науке при изучении отдаленного прошлого. Учитывается роль современной постнеклассической научной парадигмы. Выводы иллюстрируются с помощью примеров византийской истории.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.007
Scholarly communication0.0110.006
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0410.011

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 head0.014
GPT teacher head0.194
Teacher spread0.180 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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